diff --git a/docs/404.html b/docs/404.html index becc3d451..692fbba97 100644 --- a/docs/404.html +++ b/docs/404.html @@ -39,7 +39,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index af3c28f4c..50f352cb8 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 347bf2725..844078cec 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 8cb6242d8..c81b1727a 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/articles/articles-online-only/opencl.html b/docs/articles/articles-online-only/opencl.html index df160a0ad..316998bfe 100644 --- a/docs/articles/articles-online-only/opencl.html +++ b/docs/articles/articles-online-only/opencl.html @@ -40,7 +40,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/articles/cmdstanr-internals.html b/docs/articles/cmdstanr-internals.html index d034b3188..1b68fe28c 100644 --- a/docs/articles/cmdstanr-internals.html +++ b/docs/articles/cmdstanr-internals.html @@ -40,7 +40,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -183,10 +183,10 @@

Immediate compilation 
 mod$stan_file()
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.stan"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli.stan"
 mod$exe_file()
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli"

Subsequently, if you create a CmdStanModel object from the same Stan file then compilation will be skipped (assuming the file hasn’t changed).

@@ -220,7 +220,7 @@

Delayed compilation
 mod$compile()
 mod$exe_file()
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli"
+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli"

Pedantic check @@ -278,7 +278,7 @@

Pedantic check$compile() method directly if using the delayed compilation approach described above).

mod_pedantic <- cmdstan_model(stan_file_pedantic, pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a1418059d6f.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b6650db73c.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -288,7 +288,7 @@ 
Pedantic checkpedantic argument to the $check_syntax()
 method.

 
mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
     poisson distribution is given parameter lambda as a rate parameter
     (argument 1), but lambda was not constrained to be strictly positive.
 Warning: The parameter lambda has no priors. This means either no prior is
@@ -300,18 +300,18 @@ 
Pedantic check
 
file.remove(mod_pedantic$exe_file()) # delete compiled executable
-[1] TRUE

-
rm(mod_pedantic)
-
-mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE)
-mod_pedantic$check_syntax(pedantic = TRUE)
-Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
-    poisson distribution is given parameter lambda as a rate parameter
-    (argument 1), but lambda was not constrained to be strictly positive.
-Warning: The parameter lambda has no priors. This means either no prior is
-    provided, or the prior(s) depend on data variables. In the later case,
-    this may be a false positive.
-Stan program is syntactically correct

+[1] TRUE
+rm(mod_pedantic)
+
+mod_pedantic <- cmdstan_model(stan_file_pedantic, compile = FALSE)
+mod_pedantic$check_syntax(pedantic = TRUE)
+Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A
+    poisson distribution is given parameter lambda as a rate parameter
+    (argument 1), but lambda was not constrained to be strictly positive.
+Warning: The parameter lambda has no priors. This means either no prior is
+    provided, or the prior(s) depend on data variables. In the later case,
+    this may be a false positive.
+Stan program is syntactically correct

Stan model variables @@ -320,7 +320,7 @@

Stan model variables
+
 stan_file_variables <- write_stan_file("
 data {
   int<lower=1> J;
@@ -351,47 +351,47 @@ 
Stan model variables
+
 names(variables)

 
[1] "parameters"             "included_files"         "data"                  
 [4] "transformed_parameters" "generated_quantities"  

-
+
 names(variables$data)

 
[1] "J"     "sigma" "y"    

-
+
 names(variables$parameters)

 
[1] "mu"        "tau"       "theta_raw"

-
+
 names(variables$transformed_parameters)

 
[1] "theta"

-
+
 names(variables$generated_quantities)

 
character(0)

 
Each variable is represented as a list containing the type
 information (currently limited to real or int)
 and the number of dimensions.

-
+
 variables$data$J

 
$type
 [1] "int"
 
 $dimensions
 [1] 0

-
+
 variables$data$sigma

 
$type
 [1] "real"
 
 $dimensions
 [1] 1

-
+
 variables$parameters$tau

 
$type
 [1] "real"
 
 $dimensions
 [1] 0

-
+
 variables$transformed_parameters$theta

 
$type
 [1] "real"
@@ -405,7 +405,7 @@ 
Executable location
+
 mod <- cmdstan_model(stan_file, dir = "path/to/directory/for/executable")

 

 

@@ -427,7 +427,7 @@ 
Named list of R objectsN, the
 number of data points, and y an integer array of
 observations.

-
+
 mod$print()

 
data {
   int<lower=0> N;
@@ -440,7 +440,7 @@ 
Named list of R objects

-
+
 # data block has 'N' and 'y'
 data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
 fit <- mod$sample(data = data_list)

@@ -448,7 +448,7 @@ 
Named list of R objectswrite_stan_json(). This happens internally, but it is also
 possible to call write_stan_json() directly.

-
+
 data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
 json_file <- tempfile(fileext = ".json")
 write_stan_json(data_list, json_file)
@@ -465,7 +465,7 @@ 
JSON filewrite_stan_json():

-
+
 fit <- mod$sample(data = json_file)

 

 

@@ -475,7 +475,7 @@ 
R dump filerstan::stan_rdump():

-
+
 rdump_file <- tempfile(fileext = ".data.R")
 rstan::stan_rdump(names(data_list), file = rdump_file, envir = list2env(data_list))
 cat(readLines(rdump_file), sep = "\n")
@@ -488,31 +488,31 @@ 
Writing CmdStan output to CSV
 
Default temporary files
 

-
+
 data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
 fit <- mod$sample(data = data_list)

 
When fitting a model, the default behavior is to write the output
 from CmdStan to CSV files in a temporary directory.

-
+
 fit$output_files()

-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-202407021546-1-2808db.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-202407021546-2-2808db.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-202407021546-3-2808db.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-202407021546-4-2808db.csv"

+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-202503310850-1-5c7cee.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-202503310850-2-5c7cee.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-202503310850-3-5c7cee.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-202503310850-4-5c7cee.csv"

 
These files will be lost if you end your R session or if you remove
 the fit object and force (or wait for) garbage
 collection.

-
+
 files <- fit$output_files()
 file.exists(files)

 
[1] TRUE TRUE TRUE TRUE

-
+
 rm(fit)
 gc()

-
          used (Mb) gc trigger  (Mb) limit (Mb) max used (Mb)
-Ncells 1155803 61.8    2350794 125.6         NA  1542121 82.4
-Vcells 2048173 15.7    8388608  64.0      32768  2851724 21.8

-
+
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
+Ncells 1161565 62.1    2340494  125         NA  1595651 85.3
+Vcells 2051945 15.7    8388608   64      32768  4957304 37.9

+
 file.exists(files)

 
[1] FALSE FALSE FALSE FALSE

 

@@ -523,10 +523,10 @@ 
Non-temporary files
+
 # see ?save_output_files for info on optional arguments
 fit$save_output_files(dir = "path/to/directory")

-
+
 fit <- mod$sample(
   data = data_list,
   output_dir = "path/to/directory"
@@ -546,7 +546,7 @@ 
Lazy CSV readingfit object, notice how the Private slot
 draws_ is NULL, indicating that the CSV files
 haven’t yet been read into R.

-
+
 str(fit)

 
Classes 'CmdStanMCMC', 'CmdStanFit', 'R6' <CmdStanMCMC>
   Inherits from: <CmdStanFit>
@@ -605,13 +605,13 @@ 
Lazy CSV reading

 
After we call a method that requires the draws then if we reexamine
 the structure of the object we will see that the draws_
 slot in Private is no longer empty.

-
+
 
Classes 'CmdStanMCMC', 'CmdStanFit', 'R6' <CmdStanMCMC>
@@ -663,7 +663,7 @@ 
Lazy CSV readingLazy CSV reading

 
For models with many parameters, transformed parameters, or generated
@@ -686,7 +686,7 @@ 
read_cmdstan_csv()read_cmdstan_csv() function is used to
 read the CmdStan CSV files into R. This function is exposed to users, so
 you can also call it directly.

-
+
 # see ?read_cmdstan_csv for info on optional arguments controlling
 # what information is read in
 csv_contents <- read_cmdstan_csv(fit$output_files())
@@ -694,9 +694,9 @@ 
read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()read_cmdstan_csv()as_cmdstan_fit()
 
If you need to manually create fitted model objects from CmdStan CSV
 files use as_cmdstan_fit().

-
+
 fit2 <- as_cmdstan_fit(fit$output_files())

 
This is pointless in our case since we have the original
 fit object, but this function can be used to create fitted
@@ -793,32 +793,32 @@ 
Saving and
 
CmdStanR does not yet provide a special method for processing these
 files but they can be read into R using R’s standard CSV reading
 functions.

-
+
 fit <- mod$sample(data = data_list, save_latent_dynamics = TRUE)

-
+
 fit$latent_dynamics_files()

-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-diagnostic-202407021546-1-54a823.csv"
-[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-diagnostic-202407021546-2-54a823.csv"
-[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-diagnostic-202407021546-3-54a823.csv"
-[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-diagnostic-202407021546-4-54a823.csv"

-
+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-diagnostic-202503310850-1-060b43.csv"
+[2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-diagnostic-202503310850-2-060b43.csv"
+[3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-diagnostic-202503310850-3-060b43.csv"
+[4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-diagnostic-202503310850-4-060b43.csv"

+
 # read one of the files in
 x <- utils::read.csv(fit$latent_dynamics_files()[1], comment.char = "#")
 head(x)

 
      lp__ accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__
-1 -6.77025      0.987691   0.980824           2            3           0
-2 -7.20991      0.932461   0.980824           2            3           0
-3 -7.66830      0.933065   0.980824           1            1           0
-4 -7.37078      1.000000   0.980824           1            1           0
-5 -7.59961      0.948892   0.980824           2            3           0
-6 -7.95801      0.940559   0.980824           2            3           0
+1 -7.00899      0.935119    1.06923           1            3           0
+2 -7.29900      0.886772    1.06923           1            1           0
+3 -6.82892      1.000000    1.06923           1            1           0
+4 -6.82025      1.000000    1.06923           1            1           0
+5 -6.75311      0.993504    1.06923           2            3           0
+6 -6.89721      0.960772    1.06923           1            3           0
   energy__     theta   p_theta   g_theta
-1  7.08007 -0.959636 -1.160790  0.323414
-2  7.21726 -1.779880  0.178790 -1.268180
-3  7.67261 -2.089350 -0.136971 -1.678370
-4  7.66302 -1.898700 -1.127370 -1.436940
-5  8.20617 -2.047820 -1.624180 -1.628720
-6  8.67205 -2.252980  1.762220 -1.858880

+1  7.05933 -0.633893  0.440038  1.159540
+2  7.38011 -0.432323 -0.558539  1.722860
+3  7.12220 -0.836018 -1.062060  0.628489
+4  6.85073 -0.850225  0.342380  0.592629
+5  6.85434 -1.166260  0.623978 -0.149621
+6  6.92007 -0.744453  0.296526  0.864374

 
The column lp__ is also provided via
 fit$draws(), and the columns accept_stat__,
 stepsize__, treedepth__,
@@ -826,15 +826,15 @@ 
Saving and
 energy__ are also provided by
 fit$sampler_diagnostics(), but there are several columns
 unique to the latent dynamics file.

-
+
 head(x[, c("theta", "p_theta", "g_theta")])

 
      theta   p_theta   g_theta
-1 -0.959636 -1.160790  0.323414
-2 -1.779880  0.178790 -1.268180
-3 -2.089350 -0.136971 -1.678370
-4 -1.898700 -1.127370 -1.436940
-5 -2.047820 -1.624180 -1.628720
-6 -2.252980  1.762220 -1.858880

+1 -0.633893  0.440038  1.159540
+2 -0.432323 -0.558539  1.722860
+3 -0.836018 -1.062060  0.628489
+4 -0.850225  0.342380  0.592629
+5 -1.166260  0.623978 -0.149621
+6 -0.744453  0.296526  0.864374

 
Our model has a single parameter theta and the three
 columns above correspond to theta in the
 unconstrained space (theta on the constrained
@@ -877,24 +877,24 @@ 
Troubleshooting and debugging to TRUE.

-
+
 options("cmdstanr_verbose"=TRUE)
 
 mod <- cmdstan_model(stan_file, force_recompile = TRUE)

 
Running make \
-  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326 \
+  /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19 \
   "STANCFLAGS +=  --name='bernoulli_model'"
 
 --- Translating Stan model to C++ code ---
-bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.stan
+bin/stanc --name='bernoulli_model' --o=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.stan
 
 --- Compiling C++ code ---
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++17 -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.84.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS          -c -include-pch stan/src/stan/model/model_header.hpp.gch/model_header_15_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.hpp
+clang++ -O3 -march=native -mtune=native -Wno-deprecated-declarations -std=c++17 -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.84.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS          -c -include-pch stan/src/stan/model/model_header.hpp.gch/model_header_16_0.hpp.gch -x c++ -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.hpp
 
 --- Linking model ---
-clang++ -Wno-deprecated-declarations -Wno-deprecated-declarations -std=c++17 -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.84.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS               -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.35.0/stan/lib/stan_math/lib/tbb"   -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.35.0/stan/lib/stan_math/lib/tbb"      /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.o src/cmdstan/main.o       -ltbb   stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a  stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326
-rm /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.hpp /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/model-9a14560da326.o

-
+clang++ -O3 -march=native -mtune=native -Wno-deprecated-declarations -std=c++17 -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.4.0 -I stan/lib/stan_math/lib/boost_1.84.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS               -Wl,-L,"/Users/jgabry/.cmdstan/cmdstan-2.36.0/stan/lib/stan_math/lib/tbb"   -Wl,-rpath,"/Users/jgabry/.cmdstan/cmdstan-2.36.0/stan/lib/stan_math/lib/tbb"      /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.o src/cmdstan/main.o       -ltbb   stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a  stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19
+rm /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.o /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/model-d1b65860dc19.hpp

+
 fit <- mod$sample(
   data = data_list,
   chains = 1,
@@ -903,11 +903,11 @@ 
Troubleshooting and debugging)

 
Running MCMC with 1 chain...
 
-Running ./bernoulli 'id=1' random 'seed=707272264' data \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/standata-9a144d3dc244.json' \
+Running ./bernoulli 'id=1' random 'seed=1376020223' data \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/standata-d1b61e77387.json' \
   output \
-  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-202407021546-1-38ac27.csv' \
-  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2hQnhs/bernoulli-profile-202407021546-1-910909.csv' \
+  'file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-202503310850-1-1ead10.csv' \
+  'profile_file=/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpI5U8HG/bernoulli-profile-202503310850-1-3e64eb.csv' \
   'method=sample' 'num_samples=100' 'num_warmup=100' 'save_warmup=0' \
   'algorithm=hmc' 'engine=nuts' adapt 'engaged=1'
 Chain 1 method = sample (Default) 
@@ -938,20 +938,20 @@ 
Troubleshooting and debuggingTroubleshooting and debugging
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -214,10 +214,10 @@ 
Installing CmdStancmdstan_version():

 
-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0"

+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0"

 
-
[1] "2.35.0"

+
[1] "2.36.0"

 

 

 
Compiling a model
@@ -254,7 +254,7 @@ 
Compiling a model
 mod$exe_file()

-
[1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli"

+
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli"

 

 

 
Running MCMC
@@ -348,16 +348,16 @@ 
Summaries from the posterior packa
   extra_quantiles = ~posterior::quantile2(., probs = c(.0275, .975))
 )

 
  variable  mean median   sd  mad     q5   q95 rhat ess_bulk ess_tail
-1     lp__ -7.31  -7.01 0.81 0.34 -8.871 -6.75    1     1404     1631
-2    theta  0.26   0.24 0.13 0.13  0.079  0.49    1     1130     1314

+1     lp__ -7.30  -6.99 0.79 0.33 -8.927 -6.75    1     1769     1938
+2    theta  0.25   0.24 0.12 0.12  0.078  0.48    1     1228     1521

 
  variable  mean   sd
-1    theta  0.26 0.13
-2     lp__ -7.31 0.81

+1    theta  0.25 0.12
+2     lp__ -7.30 0.79

 
  variable pr_lt_half
 1    theta       0.96

 
  variable  mean median   sd  mad     q5   q95  q2.75 q97.5
-1     lp__ -7.31  -7.01 0.81 0.34 -8.871 -6.75 -9.443 -6.75
-2    theta  0.26   0.24 0.13 0.13  0.079  0.49  0.063  0.54

+1     lp__ -7.30  -6.99 0.79 0.33 -8.927 -6.75 -9.429 -6.75
+2    theta  0.25   0.24 0.12 0.12  0.078  0.48  0.062  0.54

 

 

 
CmdStan’s stansummary utility
@@ -491,7 +491,7 @@ 
Sampler diagnostic warnings a
 [1] 0 0 0 0
 
 $ebfmi
-[1] 1.11 0.80 1.05 0.95

+[1] 1.11 0.76 1.19 1.08

 
We see the number of divergences for each of the four chains, the
 number of times the maximum treedepth was hit for each chain, and the
 E-BFMI for each chain.

@@ -500,7 +500,7 @@ 
Sampler diagnostic warnings a
 suffers from divergences.

 
 fit_with_warning <- cmdstanr_example("schools")

-
Warning: 374 of 4000 (9.0%) transitions ended with a divergence.
+
Warning: 143 of 4000 (4.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.

 
Warning: 1 of 4 chains had an E-BFMI less than 0.3.
 See https://mc-stan.org/misc/warnings for details.

@@ -509,24 +509,24 @@ 
Sampler diagnostic warnings a
 fit$diagnostic_summary().

 
 diagnostics <- fit_with_warning$diagnostic_summary()

-
Warning: 374 of 4000 (9.0%) transitions ended with a divergence.
+
Warning: 143 of 4000 (4.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.

 
Warning: 1 of 4 chains had an E-BFMI less than 0.3.
 See https://mc-stan.org/misc/warnings for details.

 
 print(diagnostics)

 
$num_divergent
-[1] 269  29  11  65
+[1]  1 37 75 30
 
 $num_max_treedepth
 [1] 0 0 0 0
 
 $ebfmi
-[1] 0.15 0.34 0.38 0.37

+[1] 0.17 0.35 0.34 0.42

 
 # number of divergences reported in warning is the sum of the per chain values
 sum(diagnostics$num_divergent)

-
[1] 374

+
[1] 143

 

 

 
CmdStan’s diagnose utility
@@ -536,20 +536,6 @@ 
CmdStan’s diagnose utility
 

 

-

-
Create a stanfit object
-

-
If you have RStan installed then it is also possible to create a
-stanfit object from the csv output files written by
-CmdStan. This can be done by using rstan::read_stan_csv()
-in combination with the $output_files() method of the
-CmdStanMCMC object. This is only needed if you want to fit
-a model with CmdStanR but already have a lot of post-processing code
-that assumes a stanfit object. Otherwise we recommend using
-the post-processing functionality provided by CmdStanR itself.

-
-stanfit <- rstan::read_stan_csv(fit$output_files())

-

 

 

 
Running optimization and variational inference
@@ -565,7 +551,7 @@ 
Optimization$optimize().

-
+
 fit_mle <- mod$optimize(data = data_list, seed = 123)

 
Initial log joint probability = -16.144 
     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
@@ -573,18 +559,18 @@ 
Optimization
+
 fit_mle$print() # includes lp__ (log prob calculated by Stan program)

 
 variable estimate
     lp__     -5.00
     theta     0.20

-
+
 fit_mle$mle("theta")

 
theta 
   0.2 

 
Here’s a plot comparing the penalized MLE to the posterior
 distribution of theta.

-
+
 mcmc_hist(fit$draws("theta")) +
   vline_at(fit_mle$mle("theta"), size = 1.5)

 

@@ -595,7 +581,7 @@ 
OptimizationMaximum
 Likelihood Estimation section of the CmdStan User’s Guide for more
 details.

-
+
 fit_map <- mod$optimize(
   data = data_list,
   jacobian = TRUE,
@@ -606,7 +592,7 @@ 
Optimization
 
Laplace Approximation
@@ -624,7 +610,7 @@ 
Laplace Approximationmode argument. If mode is omitted then
 optimization will be run internally before taking draws from the normal
 approximation.

-
+
 fit_laplace <- mod$laplace(
     mode = fit_map,
     draws = 4000,
@@ -640,11 +626,11 @@ 
Laplace Approximation

-
+
 fit_laplace$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.27   0.25 0.12 0.12 0.10 0.51

-
+
 mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)

 

 

@@ -655,7 +641,7 @@ 
Variational (ADVI)$variational()
 method. For details on the ADVI algorithm see the CmdStan
 User’s Guide.

-
+
 fit_vb <- mod$variational(
   data = data_list,
   seed = 123,
@@ -666,8 +652,8 @@ 
Variational (ADVI)Variational (ADVI)
+
 fit_vb$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.26   0.24 0.11 0.11 0.11 0.46

-
+
 mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)

 

 

@@ -700,7 +686,7 @@ 
Variational (Pathfinder)CmdStan
 User’s Guide. Pathfinder is run using the $pathfinder()
 method.

-
+
 fit_pf <- mod$pathfinder(
   data = data_list,
   seed = 123,
@@ -724,7 +710,7 @@ 
Variational (Pathfinder)

-
+
 fit_pf$print("theta")

 
 variable mean median   sd  mad   q5  q95
     theta 0.25   0.24 0.12 0.12 0.08 0.47

@@ -732,22 +718,22 @@ 
Variational (Pathfinder)
-
+
 mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from variational") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
   ggplot2::xlim(0, 1)

 

-
+
 mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
   ggplot2::labs(subtitle = "Posterior from MCMC") +
   ggplot2::xlim(0, 1)

@@ -771,7 +757,7 @@ 
Saving fitted model objects
+
 fit$save_object(file = "fit.RDS")
 
 # can be read back in using readRDS
@@ -783,7 +769,7 @@ 
Saving fitted model objects$save_object()
 and replace saveRDS with the much faster
 qsave() function from the qs package.

-
+
 # Load CmdStan output files into the fitted model object.
 fit$draws() # Load posterior draws into the object.
 try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
@@ -791,23 +777,23 @@ 
Saving fitted model objectstry(fit$profiles(), silent = TRUE) # Load profiling samples.
 
 # Save the object to a file.
-qs::qsave(x = fit, file = "fit.qs")
+qs::qsave(x = fit, file = "fit.qs")
 
 # Read the object.
-fit2 <- qs::qread("fit.qs")

+fit2 <- qs::qread("fit.qs")

 
Storage is even faster if you discard results you do not need to
 save. The following example saves only posterior draws and discards
 sampler diagnostics, user-specified initial values, and profiling
 data.

-
+
 # Load posterior draws into the fitted model object and omit other output.
 fit$draws()
 
 # Save the object to a file.
-qs::qsave(x = fit, file = "fit.qs")
+qs::qsave(x = fit, file = "fit.qs")
 
 # Read the object.
-fit2 <- qs::qread("fit.qs")

+fit2 <- qs::qread("fit.qs")

 
See the vignette How
 does CmdStanR work? for more information about the composition
 of CmdStanR objects.

diff --git a/docs/articles/cmdstanr_files/figure-html/plot-compare-mcmc-1.png b/docs/articles/cmdstanr_files/figure-html/plot-compare-mcmc-1.png
index 6b4765df4..6354bf17d 100644
Binary files a/docs/articles/cmdstanr_files/figure-html/plot-compare-mcmc-1.png and b/docs/articles/cmdstanr_files/figure-html/plot-compare-mcmc-1.png differ
diff --git a/docs/articles/cmdstanr_files/figure-html/plot-mle-1.png b/docs/articles/cmdstanr_files/figure-html/plot-mle-1.png
index 8a373b96b..978cbe60e 100644
Binary files a/docs/articles/cmdstanr_files/figure-html/plot-mle-1.png and b/docs/articles/cmdstanr_files/figure-html/plot-mle-1.png differ
diff --git a/docs/articles/cmdstanr_files/figure-html/plots-1.png b/docs/articles/cmdstanr_files/figure-html/plots-1.png
index 03122c055..eb6c5d384 100644
Binary files a/docs/articles/cmdstanr_files/figure-html/plots-1.png and b/docs/articles/cmdstanr_files/figure-html/plots-1.png differ
diff --git a/docs/articles/index.html b/docs/articles/index.html
index c7286099d..24e6ec31f 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/articles/posterior.html b/docs/articles/posterior.html
index a614e8890..a62f39624 100644
--- a/docs/articles/posterior.html
+++ b/docs/articles/posterior.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -147,82 +147,80 @@ 
Summary statistics
 fit <- cmdstanr::cmdstanr_example("schools", method = "sample")

-
Warning: 124 of 4000 (3.0%) transitions ended with a divergence.
+
Warning: 145 of 4000 (4.0%) transitions ended with a divergence.
 See https://mc-stan.org/misc/warnings for details.

-
Warning: 1 of 4 chains had an E-BFMI less than 0.3.
-See https://mc-stan.org/misc/warnings for details.

-
+
 fit$summary()

-
   variable       mean     median       sd      mad          q5       q95
-1      lp__ -58.697829 -58.911800 5.056327 5.134318 -66.6875850 -49.89660
-2        mu   6.353763   6.597480 4.381424 4.372565  -1.2281605  13.23484
-3       tau   5.604807   4.871790 3.537844 3.371217   1.4006485  12.28454
-4  theta[1]   9.416434   8.789030 7.469694 6.297677  -1.7534945  22.77244
-5  theta[2]   6.708629   6.756395 5.775008 5.428570  -2.7675325  16.09375
-6  theta[3]   5.013658   5.633410 7.082039 6.055554  -7.5991140  15.69620
-7  theta[4]   6.479275   6.629210 6.113995 5.602212  -3.7474250  16.47548
-8  theta[5]   4.415845   4.751705 5.837104 5.557445  -5.7668200  13.46467
-9  theta[6]   5.316444   5.776025 6.132648 5.695771  -5.1632545  14.89951
-10 theta[7]   9.159755   8.682990 6.276413 5.676549  -0.3210604  20.38977
-11 theta[8]   7.114358   7.154305 6.994382 6.108230  -4.2959180  18.25591
+
   variable       mean     median       sd      mad           q5       q95
+1      lp__ -58.189615 -58.233950 4.983507 5.199033 -66.09672000 -49.48569
+2        mu   6.770868   6.720615 4.160694 4.104430   0.05392642  13.50031
+3       tau   5.360230   4.524270 3.536662 3.128657   1.41195350  12.01772
+4  theta[1]   9.538150   8.703025 6.992732 6.139536  -0.46183645  21.76395
+5  theta[2]   7.037750   7.084940 5.672420 5.463211  -1.89471350  16.19477
+6  theta[3]   5.865357   5.940265 6.567071 5.598943  -4.94042600  15.87788
+7  theta[4]   7.096898   7.021110 5.846662 5.507303  -2.11556450  16.63653
+8  theta[5]   4.905340   5.104415 5.567945 5.102183  -4.69825650  13.42720
+9  theta[6]   5.743860   5.979900 5.965230 5.476702  -4.29255000  15.06765
+10 theta[7]   9.332145   8.785955 5.911919 5.517199   0.73314065  20.11738
+11 theta[8]   7.283320   7.100935 6.510593 5.937568  -3.18226350  18.23657
        rhat  ess_bulk  ess_tail
-1  1.018582  172.4200  135.9782
-2  1.008425  635.3354 1168.1366
-3  1.021625  165.4672  120.1282
-4  1.003830 1073.3788 1319.6507
-5  1.004990 1050.8445 1693.6799
-6  1.007712  871.0003 1537.3018
-7  1.003560 1027.3246 1869.0154
-8  1.007430  780.1467 1587.7245
-9  1.007162  917.8167 1453.6511
-10 1.003741  956.3136 1718.3496
-11 1.002369 1338.3238 1986.1357

+1  1.029985  154.8234  166.9326
+2  1.004450  669.4002 1293.6704
+3  1.029747  147.4551  128.0359
+4  1.006213 1068.8761 2337.7828
+5  1.002627 1067.0310 2241.5417
+6  1.004902 1217.1050 1912.7529
+7  1.002033 1030.8586 1665.2932
+8  1.009740  637.5400 1725.4378
+9  1.003866 1129.6591 2064.6751
+10 1.003642  989.5674 1661.8675
+11 1.005570 1163.3998 1970.5629

 
By default all variables are summaries with the follow functions:

-
+
 
[1] "mean"      "median"    "sd"        "mad"       "quantile2"

 
To change the variables summarized, we use the variables argument

-
+
 fit$summary(variables = c("mu", "tau"))

-
  variable     mean  median       sd      mad        q5      q95     rhat
-1       mu 6.353763 6.59748 4.381424 4.372565 -1.228160 13.23484 1.008425
-2      tau 5.604807 4.87179 3.537844 3.371217  1.400649 12.28454 1.021625
+
  variable     mean   median       sd      mad         q5      q95     rhat
+1       mu 6.770868 6.720615 4.160694 4.104430 0.05392642 13.50031 1.004450
+2      tau 5.360230 4.524270 3.536662 3.128657 1.41195350 12.01772 1.029747
   ess_bulk  ess_tail
-1 635.3354 1168.1366
-2 165.4672  120.1282

+1 669.4002 1293.6704
+2 147.4551  128.0359

 
We can additionally change which functions are used

-
+
 fit$summary(variables = c("mu", "tau"), mean, sd)

 
  variable     mean       sd
-1       mu 6.353763 4.381424
-2      tau 5.604807 3.537844

+1       mu 6.770868 4.160694
+2      tau 5.360230 3.536662

 
To summarize all variables with non-default functions, it is
 necessary to set explicitly set the variables argument, either to
 NULL or the full vector of variable names.

-
+
 fit$metadata()$model_params

 
 [1] "lp__"     "mu"       "tau"      "theta[1]" "theta[2]" "theta[3]"
  [7] "theta[4]" "theta[5]" "theta[6]" "theta[7]" "theta[8]"

-
+
 fit$summary(variables = NULL, "mean", "median")

 
   variable       mean     median
-1      lp__ -58.697829 -58.911800
-2        mu   6.353763   6.597480
-3       tau   5.604807   4.871790
-4  theta[1]   9.416434   8.789030
-5  theta[2]   6.708629   6.756395
-6  theta[3]   5.013658   5.633410
-7  theta[4]   6.479275   6.629210
-8  theta[5]   4.415845   4.751705
-9  theta[6]   5.316444   5.776025
-10 theta[7]   9.159755   8.682990
-11 theta[8]   7.114358   7.154305

+1      lp__ -58.189615 -58.233950
+2        mu   6.770868   6.720615
+3       tau   5.360230   4.524270
+4  theta[1]   9.538150   8.703025
+5  theta[2]   7.037750   7.084940
+6  theta[3]   5.865357   5.940265
+7  theta[4]   7.096898   7.021110
+8  theta[5]   4.905340   5.104415
+9  theta[6]   5.743860   5.979900
+10 theta[7]   9.332145   8.785955
+11 theta[8]   7.283320   7.100935

 
Summary functions can be specified by character string, function, or
 using a formula (or anything else supported by
 rlang::as_function()). If these arguments are named, those
 names will be used in the tibble output. If the summary results are
 named they will take precedence.

-
+
 my_sd <- function(x) c(My_SD = sd(x))
 fit$summary(
   c("mu", "tau"), 
@@ -232,45 +230,45 @@ 
Summary statistics~quantile(.x, probs = c(0.1, 0.9)),
   Minimum = function(x) min(x)
 )        

-
  variable     MEAN  median    My_SD      10%      90%   Minimum
-1       mu 6.353763 6.59748 4.381424 0.756247 11.71914 -9.340540
-2      tau 5.604807 4.87179 3.537844 1.723530 10.46083  0.749924

+
  variable     MEAN   median    My_SD      10%      90%   Minimum
+1       mu 6.770868 6.720615 4.160694 1.577812 12.27689 -7.905940
+2      tau 5.360230 4.524270 3.536662 1.751609 10.20606  0.836362

 
Arguments to all summary functions can also be specified with
 .args.

-
+
 fit$summary(c("mu", "tau"), quantile, .args = list(probs = c(0.025, .05, .95, .975)))

-
  variable      2.5%        5%      95%    97.5%
-1       mu -2.926103 -1.228160 13.23484 14.96275
-2      tau  1.106000  1.400649 12.28454 14.09220

+
  variable      2.5%         5%      95%    97.5%
+1       mu -1.320617 0.05392642 13.50031 14.99084
+2      tau  1.176680 1.41195350 12.01772 13.99329

 
The summary functions are applied to the array of sample values, with
 dimension iter_samplingxchains.

-
+
 fit$summary(variables = NULL, dim, colMeans)

 
   variable dim.1 dim.2          1          2          3          4
-1      lp__  1000     4 -57.746884 -58.396713 -59.488281 -59.159440
-2        mu  1000     4   6.886773   6.098293   6.275602   6.154385
-3       tau  1000     4   4.969696   5.504146   6.035791   5.909594
-4  theta[1]  1000     4   9.584919   8.855289   9.788069   9.437459
-5  theta[2]  1000     4   7.218764   6.441512   6.701321   6.472920
-6  theta[3]  1000     4   6.039431   4.880572   4.532884   4.601744
-7  theta[4]  1000     4   6.923818   6.048362   6.708068   6.236851
-8  theta[5]  1000     4   5.339809   4.243510   3.959413   4.120650
-9  theta[6]  1000     4   5.971513   4.851822   5.284337   5.158102
-10 theta[7]  1000     4   9.310560   8.904282   9.350879   9.073298
-11 theta[8]  1000     4   7.380020   7.069916   7.245772   6.761726

+1      lp__  1000     4 -59.512825 -57.501953 -58.519416 -57.224267
+2        mu  1000     4   6.687580   7.323996   6.485391   6.586506
+3       tau  1000     4   6.113202   4.991316   5.617574   4.718827
+4  theta[1]  1000     4   9.845526  10.063756   9.480842   8.762475
+5  theta[2]  1000     4   7.109487   7.340212   6.950864   6.750437
+6  theta[3]  1000     4   5.614556   6.465821   5.368069   6.012981
+7  theta[4]  1000     4   7.095362   7.575608   6.862193   6.854430
+8  theta[5]  1000     4   4.335314   5.915169   4.347114   5.023764
+9  theta[6]  1000     4   5.495814   6.275650   5.270746   5.933231
+10 theta[7]  1000     4   9.739179   9.685921   9.144607   8.758873
+11 theta[8]  1000     4   7.154340   7.740947   7.134879   7.103115

 
For this reason users may have unexpected results if they use
 stats::var() directly, as it will return a covariance
 matrix. An alternative is the distributional::variance()
 function, which can also be accessed via
 posterior::variance().

-
+
 fit$summary(c("mu", "tau"), posterior::variance, ~var(as.vector(.x)))

 
  variable posterior::variance ~var(as.vector(.x))
-1       mu            19.19687            19.19687
-2      tau            12.51634            12.51634

+1       mu            17.31137            17.31137
+2      tau            12.50798            12.50798

 
Summary functions need not be numeric, but these won’t work with
 $print().

-
+
 strict_pos <- function(x) if (all(x > 0)) "yes" else "no"
 fit$summary(variables = NULL, "Strictly Positive" = strict_pos)

 
   variable Strictly Positive
@@ -285,7 +283,7 @@ 
Summary statistics
+
 # fit$print(variables = NULL, "Strictly Positive" = strict_pos)

 
For more information, see posterior::summarise_draws(),
 which is called by $summary().

@@ -299,49 +297,49 @@ 
Extracting posterior draws/samplesdraws_array and
 draws_df formats, but the posterior
 package supports other useful formats as well.

-
+
-
 'draws_array' num [1:1000, 1:4, 1:11] -66.1 -61.6 -58.3 -57.5 -55.3 ...
+
 'draws_array' num [1:1000, 1:4, 1:11] -60.3 -61 -59.5 -58.6 -64.2 ...
  - attr(*, "dimnames")=List of 3
   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
   ..$ chain    : chr [1:4] "1" "2" "3" "4"
   ..$ variable : chr [1:11] "lp__" "mu" "tau" "theta[1]" ...

-
+
 # draws x variables data frame
 draws_df <- fit$draws(format = "df")
 str(draws_df)

 
draws_df [4,000 × 14] (S3: draws_df/draws/tbl_df/tbl/data.frame)
- $ lp__      : num [1:4000] -66.1 -61.6 -58.3 -57.5 -55.3 ...
- $ mu        : num [1:4000] 1.93 8.57 4.05 9.53 9.94 ...
- $ tau       : num [1:4000] 10.73 6.28 6.1 4.65 3.2 ...
- $ theta[1]  : num [1:4000] 9.1 21.74 11.18 13.36 9.68 ...
- $ theta[2]  : num [1:4000] 11.006 5.842 -0.815 17.943 11.148 ...
- $ theta[3]  : num [1:4000] 0.4 12.17 7.94 6.17 3.94 ...
- $ theta[4]  : num [1:4000] -3.74 14.86 6.59 9.74 7.95 ...
- $ theta[5]  : num [1:4000] 12.97 5.5 1.32 8.77 12.83 ...
- $ theta[6]  : num [1:4000] -11.04 1.81 6.18 6.33 6.56 ...
- $ theta[7]  : num [1:4000] 22.21 10.53 7.75 14.3 11.54 ...
- $ theta[8]  : num [1:4000] 14.77 -2.98 11.19 7.25 10.37 ...
+ $ lp__      : num [1:4000] -60.3 -61 -59.5 -58.6 -64.2 ...
+ $ mu        : num [1:4000] 13.44 3.8 5.99 5.51 10.76 ...
+ $ tau       : num [1:4000] 5.55 7.85 6.33 5.48 8.88 ...
+ $ theta[1]  : num [1:4000] 18.07 3.52 12.12 4.46 13.9 ...
+ $ theta[2]  : num [1:4000] 16.476 -0.445 4.075 3.544 4.102 ...
+ $ theta[3]  : num [1:4000] 11.7279 5.8634 -0.0525 -0.6551 3.4664 ...
+ $ theta[4]  : num [1:4000] 14.84 3.94 -1.12 6.6 -2.9 ...
+ $ theta[5]  : num [1:4000] 12.108 0.652 -2.931 7.275 -6.486 ...
+ $ theta[6]  : num [1:4000] 7.16 9.55 9.69 3.59 6.77 ...
+ $ theta[7]  : num [1:4000] 11.31 11.43 4.64 11.3 5.55 ...
+ $ theta[8]  : num [1:4000] 24.13 -7.81 9.13 -3.8 20.05 ...
  $ .chain    : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
  $ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
  $ .draw     : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...

-
+
 print(draws_df)

 
# A draws_df: 1000 iterations, 4 chains, and 11 variables
    lp__   mu  tau theta[1] theta[2] theta[3] theta[4] theta[5]
-1   -66  1.9 10.7      9.1    11.01      0.4     -3.7     13.0
-2   -62  8.6  6.3     21.7     5.84     12.2     14.9      5.5
-3   -58  4.1  6.1     11.2    -0.82      7.9      6.6      1.3
-4   -58  9.5  4.6     13.4    17.94      6.2      9.7      8.8
-5   -55  9.9  3.2      9.7    11.15      3.9      7.9     12.8
-6   -55  9.9  4.0      8.5     8.81      6.6      9.8     12.0
-7   -54  9.7  2.7     14.5    10.87      5.8      6.8     10.2
-8   -52  8.7  2.2     10.5    10.42      9.4      9.8      9.4
-9   -57 10.1  5.0     13.4    10.29     11.4      5.8     12.0
-10  -58 10.5  5.5     14.8     9.45     13.9      5.6     12.0
+1   -60 13.4  5.6     18.1    16.48   11.728     14.8    12.11
+2   -61  3.8  7.9      3.5    -0.45    5.863      3.9     0.65
+3   -59  6.0  6.3     12.1     4.08   -0.053     -1.1    -2.93
+4   -59  5.5  5.5      4.5     3.54   -0.655      6.6     7.28
+5   -64 10.8  8.9     13.9     4.10    3.466     -2.9    -6.49
+6   -61  6.6 11.3     19.3    10.54    6.469      5.6    -3.64
+7   -49  7.2  2.1      5.6     6.76    6.771      7.5     7.51
+8   -53  6.8  1.5      6.1     6.97    7.150      3.8     8.49
+9   -53  6.8  1.5      6.1     6.97    7.150      3.8     8.49
+10  -56  6.5  3.8      4.8     3.31    9.891      8.5     4.73
 # ... with 3990 more draws, and 3 more variables
 # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

 
To convert an existing draws object to a different format use the
@@ -364,7 +362,7 @@ 
Structured draws similar to x. To instead directly
 access the draws of x while maintaining the structure of
 the matrix use posterior::draws_of(). For example:

-
+
 draws <- posterior::as_draws_rvars(fit$draws())
 x_rvar <- draws$x
 x_array <- posterior::draws_of(draws$x)

diff --git a/docs/articles/profiling.html b/docs/articles/profiling.html
index 2336b31c3..dd98c4330 100644
--- a/docs/articles/profiling.html
+++ b/docs/articles/profiling.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -250,11 +250,11 @@ 
Accessing the profiling info
 fit$profiles()

 
[[1]]
         name      thread_id total_time forward_time reverse_time chain_stack
-1     priors 0x7ff858f85100 0.00636262    0.0041022   0.00226042       34634
-2 likelihood 0x7ff858f85100 0.92346400    0.7530890   0.17037500       51951
+1 likelihood 0x7ff85af4eb00 0.60053000   0.49063200   0.10989800       52272
+2     priors 0x7ff85af4eb00 0.00611123   0.00426232   0.00184891       34848
   no_chain_stack autodiff_calls no_autodiff_calls
-1          34634          17317                 1
-2       34651317          17317                 1

+1       34865424          17424                 1
+2          34848          17424                 1

 
The total_time column is the total time spent inside a
 given profile statement. It is clear that the vast majority of time is
 spent in the likelihood function.

@@ -297,11 +297,11 @@ 
Comparing to a faster versio
 fit_glm$profiles()

 
[[1]]
         name      thread_id total_time forward_time reverse_time chain_stack
-1     priors 0x7ff858f85100 0.00547866   0.00378008   0.00169859       34894
-2 likelihood 0x7ff858f85100 0.55301400   0.55111300   0.00190093       52341
+1 likelihood 0x7ff85af4eb00 0.30010000   0.29864700   0.00145329       51729
+2     priors 0x7ff85af4eb00 0.00520466   0.00393537   0.00126928       34486
   no_chain_stack autodiff_calls no_autodiff_calls
-1          34894          17447                 1
-2          17447          17447                 1

+1          17243          17243                 1
+2          34486          17243                 1

 
We can see from the total_time column that this is much
 faster than the previous model.

 

@@ -324,7 +324,7 @@ 
Per-gradient timings, and memory
 profile_chain_1 <- fit$profiles()[[1]]
 per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls
 print(per_gradient_timing) # two elements for the two profile statements in the model

-
[1] 3.674205e-07 5.332702e-05

+
[1] 3.446568e-05 3.507363e-07

 

 

 
Accessing and saving the profile files
@@ -335,7 +335,7 @@ 
Accessing and saving the profile
 $profile_files().

 
 fit$profile_files()

-
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpEbNhyd/model_6580008f67848265f3dfd0e7ae3b0600-profile-202407021547-1-96d3d3.csv"

+
[1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp2a6FE1/model_6580008f67848265f3dfd0e7ae3b0600-profile-202503310851-1-810271.csv"

 
These can be saved to a more permanent location with the
 $save_profile_files() method.

 
diff --git a/docs/articles/r-markdown.html b/docs/articles/r-markdown.html
index 2f9ac3f1a..a6d479bac 100644
--- a/docs/articles/r-markdown.html
+++ b/docs/articles/r-markdown.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -229,14 +229,13 @@ 
Example#> 
 #> All 4 chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.7 seconds.

-
-
+#> Total execution time: 0.7 seconds.
+
 print(fit)
 #>  variable  mean median   sd  mad    q5   q95 rhat ess_bulk ess_tail
-#>      lp__ -1.55  -1.24 1.24 1.04 -4.07 -0.18 1.00     1509     1671
-#>      y[1] -0.03  -0.03 1.01 0.99 -1.69  1.68 1.00     1852     2195
-#>      y[2] -0.13  -0.07 2.92 2.14 -4.92  4.65 1.00     1855     1666

+#>      lp__ -1.49  -1.15 1.25 0.98 -4.04 -0.17 1.00     1284     1327
+#>      y[1] -0.03  -0.02 1.01 1.02 -1.68  1.62 1.00     2086     1813
+#>      y[2] -0.01  -0.05 2.78 1.99 -4.55  4.62 1.00     2015     1281

 

 

 
Option 3: Using both RStan and CmdStanR in the same R Markdown
@@ -248,26 +247,26 @@ 

 project, the option to use both exists. When registering CmdStanR’s
 knitr engine, set override = FALSE to register the engine
 as a cmdstan engine:

-
+
 register_knitr_engine(override = FALSE)

 
This will cause stan chunks to be processed by knitr’s
 built-in, RStan-based engine and only use CmdStanR’s knitr engine for
 cmdstan chunks:

-
```{stan, output.var="model_obj1"}
-// Results in a stanmodel object from RStan
-```
-
-```{r}
-rstan::sampling(model_obj1)
-```
-
-```{cmdstan, output.var="model_obj2"}
-// Results in a CmdStanModel object from CmdStanR
-```
-
-```{r}
-model_obj2$sample()
-```

+
```{stan, output.var="model_obj1"}
+// Results in a stanmodel object from RStan
+```
+
+```{r}
+rstan::sampling(model_obj1)
+```
+
+```{cmdstan, output.var="model_obj2"}
+// Results in a CmdStanModel object from CmdStanR
+```
+
+```{r}
+model_obj2$sample()
+```

 

 

 
Caching chunks
diff --git a/docs/authors.html b/docs/authors.html
index fb693ea93..3c863bf25 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -160,15 +160,15 @@ 
Citation

     

 
 
-    
Gabry J, Češnovar R, Johnson A, Bronder S (2024).
+    
Gabry J, Češnovar R, Johnson A, Bronder S (2025).
 cmdstanr: R Interface to 'CmdStan'.
-R package version 0.8.1, https://discourse.mc-stan.org, https://mc-stan.org/cmdstanr/. 
+R package version 0.9.0, https://discourse.mc-stan.org, https://mc-stan.org/cmdstanr/. 
 

     
@Manual{,
   title = {cmdstanr: R Interface to 'CmdStan'},
   author = {Jonah Gabry and Rok Češnovar and Andrew Johnson and Steve Bronder},
-  year = {2024},
-  note = {R package version 0.8.1, https://discourse.mc-stan.org},
+  year = {2025},
+  note = {R package version 0.9.0, https://discourse.mc-stan.org},
   url = {https://mc-stan.org/cmdstanr/},
 }

 
diff --git a/docs/index.html b/docs/index.html
index de070a6d2..2f0377a4d 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -40,7 +40,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/news/index.html b/docs/news/index.html
index b66cb1ab7..6fd3e6cd7 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -102,6 +102,40 @@ 
Changelog 

       Source: NEWS.md
     

 
+    

+
cmdstanr 0.9.0

+

+
General Improvements/Changes

+
  • Added compatibility for RTools45 (#1066)
    • 
+
  • cmdstanr will now use RTools with no additional toolchain updates needed on Windows (CmdStan 2.35+ only; #1065, #1054)
    • 
+
  • Improve error messages when calling sampler_diagnostics() with fixed_param=TRUE
+
    • 
+
  • Improve numerical stability in calculation of effective sample size during loo method (#1057)
    • 
+
  • Improve numerical stablity with very small log-ratios in calculation of effective sample size during loo method (#1015)
    • 
+
  • Add warning if input data/inits have been coerced to ints (#994)
    • 
+

+

+
Bugfixes

+
  • Don’t require fixed_param for models with zero parameters (only GQs) for CmdStan >= 2.36 (#1046)
    • 
+
  • Improve detection/handling of make (#1036)
    • 
+
  • Fix saving of model objects to network drive (#1038, thanks to @bschneidr)
    • 
+
  • Update usage of untar to fix installation errors (#1034)
    • 
+
  • Respect compilation flags in make/local when exposing functions or model methods (#1003)
    • 
+
  • Fix passing of include paths to CmdStan (#1000)
    • 
+
  • Fix passing of factor data to CmdStan (#999)
    • 
+
  • Fix extraction and passing of array data/parameters as model inits (#993)
    • 
+

+

+
Documentation Updates

+
  • Clarifications to usage of optimize and loo methods (#1060)
    • 
+
  • Add documentation for faster model saving with large models (#1042)
    • 
+
  • Remove mentions of rstan::read_stan_csv due to incompatibility with newer CmdStan outputs (#1018)
    • 
+
  • Document global option cmdstanr_print_line_numbers for printing line numbers (#1017)
    • 
+
  • Change usage of ‘chapter’ to ‘section’ in documentation (#1014)
    • 
+
  • Remove examples of updating removed array syntax as functionality no longer supported in CmdStan (#1008)
    • 
+
  • Change usages of ‘sampling statement’ -> ‘distribution statement’ (#987)
    • 
+

+

     

 
cmdstanr 0.8.1

 

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 427640f5e..2d0ef80ca 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,4 +1,4 @@
-pandoc: 3.1.11
+pandoc: '3.2'
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
@@ -8,7 +8,7 @@ articles:
   posterior: posterior.html
   profiling: profiling.html
   r-markdown: r-markdown.html
-last_built: 2024-07-02T21:30Z
+last_built: 2025-03-31T14:41Z
 urls:
   reference: https://mc-stan.org/cmdstanr/reference
   article: https://mc-stan.org/cmdstanr/articles
diff --git a/docs/reference/CmdStanDiagnose.html b/docs/reference/CmdStanDiagnose.html
index 8a2a8930b..2efca167b 100644
--- a/docs/reference/CmdStanDiagnose.html
+++ b/docs/reference/CmdStanDiagnose.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -137,11 +137,11 @@ 
Examples

 
 # retrieve the gradients
 test$gradients()
-#>   param_idx     value      model finite_diff        error
-#> 1         0 -0.656568  21.488200   21.488200 -3.43539e-08
-#> 2         1  0.536526 -22.101600  -22.101600 -6.85812e-10
-#> 3         2  1.603540 -33.634400  -33.634400  1.93245e-08
-#> 4         3  0.609685   0.311848    0.311848 -1.15926e-08
+#>   param_idx     value    model finite_diff       error
+#> 1         0  1.025820 -9.58652    -9.58652 1.06300e-08
+#> 2         1 -1.330520  7.42547     7.42547 3.40819e-08
+#> 3         2 -1.187440 13.23460    13.23460 7.67016e-09
+#> 4         3  0.699258  3.50023     3.50023 6.99257e-09
 # }
 
 

diff --git a/docs/reference/CmdStanGQ.html b/docs/reference/CmdStanGQ.html
index 6c879fc14..4eb00470b 100644
--- a/docs/reference/CmdStanGQ.html
+++ b/docs/reference/CmdStanGQ.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -177,7 +177,7 @@ 
Examples

 #> 
 #> All 4 chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.9 seconds.
+#> Total execution time: 0.7 seconds.
 #> 
 
 # stan program for standalone generated quantities
@@ -206,7 +206,7 @@ 
Examples

 #> 
 #> All 4 chains finished successfully.
 #> Mean chain execution time: 0.0 seconds.
-#> Total execution time: 0.6 seconds.
+#> Total execution time: 0.5 seconds.
 str(fit_gq$draws())
 #>  'draws_array' int [1:1000, 1:4, 1:10] 0 0 0 1 1 0 1 1 0 1 ...
 #>  - attr(*, "dimnames")=List of 3
@@ -215,7 +215,7 @@ 
Examples

 #>   ..$ variable : chr [1:10] "y_rep[1]" "y_rep[2]" "y_rep[3]" "y_rep[4]" ...
 
 library(posterior)
-#> This is posterior version 1.6.0
+#> This is posterior version 1.6.1
 #> 
 #> Attaching package: ‘posterior’
 #> The following objects are masked from ‘package:stats’:
diff --git a/docs/reference/CmdStanLaplace.html b/docs/reference/CmdStanLaplace.html
index 7115dbb92..613716534 100644
--- a/docs/reference/CmdStanLaplace.html
+++ b/docs/reference/CmdStanLaplace.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/CmdStanMCMC.html b/docs/reference/CmdStanMCMC.html
index 9b2b7b9c0..a74ccf1d6 100644
--- a/docs/reference/CmdStanMCMC.html
+++ b/docs/reference/CmdStanMCMC.html
@@ -20,7 +20,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/CmdStanMLE.html b/docs/reference/CmdStanMLE.html
index 15e511707..025570d1e 100644
--- a/docs/reference/CmdStanMLE.html
+++ b/docs/reference/CmdStanMLE.html
@@ -1,6 +1,11 @@
 
 CmdStanMLE objects — CmdStanMLE • cmdstanr
@@ -18,7 +23,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -106,7 +111,12 @@ 
CmdStanMLE objects

 
     

     
A CmdStanMLE object is the fitted model object returned by the
-$optimize() method of a CmdStanModel object.

+$optimize() method of a CmdStanModel object.
+This object will either contain a penalized maximum likelihood estimate
+(MLE) or a maximum a posteriori estimate (MAP), depending on the value of
+the jacobian argument when the model is fit (and whether the model has
+constrained parameters). See $optimize() and the
+CmdStan User's Guide for more details.

     

 
 
diff --git a/docs/reference/CmdStanModel-1.png b/docs/reference/CmdStanModel-1.png
index aa185638c..35206a7af 100644
Binary files a/docs/reference/CmdStanModel-1.png and b/docs/reference/CmdStanModel-1.png differ
diff --git a/docs/reference/CmdStanModel-2.png b/docs/reference/CmdStanModel-2.png
index 4cf81fa80..b078570c6 100644
Binary files a/docs/reference/CmdStanModel-2.png and b/docs/reference/CmdStanModel-2.png differ
diff --git a/docs/reference/CmdStanModel-3.png b/docs/reference/CmdStanModel-3.png
index c67b37ed1..35367ed42 100644
Binary files a/docs/reference/CmdStanModel-3.png and b/docs/reference/CmdStanModel-3.png differ
diff --git a/docs/reference/CmdStanModel.html b/docs/reference/CmdStanModel.html
index ed53020c7..9b9494188 100644
--- a/docs/reference/CmdStanModel.html
+++ b/docs/reference/CmdStanModel.html
@@ -20,7 +20,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -150,7 +150,7 @@ 
Examples

 library(cmdstanr)
 library(posterior)
 library(bayesplot)
-#> This is bayesplot version 1.11.1
+#> This is bayesplot version 1.11.1.9000
 #> - Online documentation and vignettes at mc-stan.org/bayesplot
 #> - bayesplot theme set to bayesplot::theme_default()
 #>    * Does _not_ affect other ggplot2 plots
@@ -168,7 +168,7 @@ 
Examples

 # Otherwise use the `path` argument to specify the location of your
 # CmdStan installation.)
 set_cmdstan_path(path = NULL)
-#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0
+#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0
 
 # Create a CmdStanModel object from a Stan program,
 # here using the example model that comes with CmdStan
@@ -270,8 +270,8 @@ 
Examples

 #> # A tibble: 2 × 10
 #>   variable   mean median    sd   mad      q5    q95  rhat ess_bulk ess_tail
 #>   <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
-#> 1 lp__     -7.30  -7.00  0.811 0.344 -8.83   -6.75   1.00     702.     776.
-#> 2 theta     0.254  0.238 0.125 0.124  0.0807  0.483  1.00     634.     580.
+#> 1 lp__     -7.35  -7.01  0.882 0.353 -9.14   -6.75   1.00     724.     896.
+#> 2 theta     0.254  0.239 0.129 0.126  0.0737  0.488  1.00     532.     657.
 
 # Check sampling diagnostics
 fit_mcmc$diagnostic_summary()
@@ -282,7 +282,7 @@ 
Examples

 #> [1] 0 0
 #> 
 #> $ebfmi
-#> [1] 1.1148699 0.8012192
+#> [1] 1.1148479 0.7568734
 #> 
 
 # Get posterior draws
@@ -292,22 +292,22 @@ 
Examples

 #> , , variable = lp__
 #> 
 #>          chain
-#> iteration    1     2
-#>         1 -7.0  -6.8
-#>         2 -7.9  -6.9
-#>         3 -7.4  -6.8
-#>         4 -6.7  -6.8
-#>         5 -6.9 -10.2
+#> iteration    1    2
+#>         1 -7.0 -8.1
+#>         2 -7.9 -7.9
+#>         3 -7.4 -7.0
+#>         4 -6.7 -6.8
+#>         5 -6.9 -6.8
 #> 
 #> , , variable = theta
 #> 
 #>          chain
-#> iteration    1    2
-#>         1 0.17 0.23
-#>         2 0.46 0.18
-#>         3 0.41 0.28
-#>         4 0.25 0.23
-#>         5 0.18 0.62
+#> iteration    1     2
+#>         1 0.17 0.088
+#>         2 0.46 0.097
+#>         3 0.41 0.167
+#>         4 0.25 0.292
+#>         5 0.18 0.238
 #> 
 #> # ... with 995 more iterations
 
@@ -333,11 +333,6 @@ 
Examples

 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
-# For models fit using MCMC, if you like working with RStan's stanfit objects
-# then you can create one with rstan::read_stan_csv()
-# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
-
-
 # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
 # and also demonstrate specifying data as a path to a file instead of a list
 my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
@@ -358,9 +353,9 @@ 
Examples

 # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -17.5579 
+#> Initial log joint probability = -6.93289 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        5      -6.74802    0.00114604   2.88021e-05           1           1        8    
+#>        4      -6.74802    0.00149466   1.90231e-05           1           1        7    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
 #> Finished in  0.1 seconds.
@@ -393,9 +388,9 @@ 
Examples

 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
-#> 1 lp__        -7.25  -6.96  0.761 0.290 -8.64  -6.75   
-#> 2 lp_approx__ -0.505 -0.216 0.751 0.296 -1.96  -0.00191
-#> 3 theta        0.269  0.248 0.124 0.119  0.102  0.505  
+#> 1 lp__        -7.24  -6.96  0.738 0.292 -8.68  -6.75   
+#> 2 lp_approx__ -0.494 -0.213 0.716 0.294 -2.00  -0.00155
+#> 3 theta        0.268  0.245 0.124 0.116  0.104  0.509  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -404,8 +399,8 @@ 
Examples

 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 1.6e-05 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds. 
+#> Gradient evaluation took 1.1e-05 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -469,48 +464,47 @@ 
Examples

 fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
                          history_size=50, max_lbfgs_iters=100)
 #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. 
-#> Path [1] :Initial log joint density = -7.658980 
+#> Path [1] :Initial log joint density = -7.264402 
 #> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.493e-04   7.219e-07    1.000e+00  1.000e+00       126 -6.255e+00 -6.255e+00                   
-#> Path [1] :Best Iter: [3] ELBO (-6.118635) evaluations: (126) 
-#> Path [2] :Initial log joint density = -15.590813 
+#>               5      -6.748e+00      3.293e-04   4.141e-07    1.000e+00  1.000e+00       126 -6.296e+00 -6.296e+00                   
+#> Path [1] :Best Iter: [4] ELBO (-6.235406) evaluations: (126) 
+#> Path [2] :Initial log joint density = -11.117345 
 #> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.968e-03   5.939e-05    1.000e+00  1.000e+00       126 -6.158e+00 -6.158e+00                   
-#> Path [2] :Best Iter: [5] ELBO (-6.157859) evaluations: (126) 
-#> Path [3] :Initial log joint density = -6.799846 
+#>               5      -6.748e+00      9.672e-04   1.459e-05    1.000e+00  1.000e+00       126 -6.259e+00 -6.259e+00                   
+#> Path [2] :Best Iter: [2] ELBO (-6.180823) evaluations: (126) 
+#> Path [3] :Initial log joint density = -7.495731 
 #> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      2.833e-04   1.189e-06    1.000e+00  1.000e+00       101 -6.196e+00 -6.196e+00                   
-#> Path [3] :Best Iter: [4] ELBO (-6.196377) evaluations: (101) 
-#> Path [4] :Initial log joint density = -7.264152 
+#>               5      -6.748e+00      1.114e-04   4.492e-07    1.000e+00  1.000e+00       126 -6.259e+00 -6.259e+00                   
+#> Path [3] :Best Iter: [2] ELBO (-6.245533) evaluations: (126) 
+#> Path [4] :Initial log joint density = -7.770449 
 #> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      4.881e-03   1.363e-04    1.000e+00  1.000e+00       101 -6.246e+00 -6.246e+00                   
-#> Path [4] :Best Iter: [4] ELBO (-6.246482) evaluations: (101) 
-#> Path [5] :Initial log joint density = -6.872687 
+#>               5      -6.748e+00      1.750e-04   9.341e-07    1.000e+00  1.000e+00       126 -6.225e+00 -6.225e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.225361) evaluations: (126) 
+#> Path [5] :Initial log joint density = -14.218076 
 #> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      9.087e-04   8.299e-06    1.000e+00  1.000e+00       101 -6.154e+00 -6.154e+00                   
-#> Path [5] :Best Iter: [4] ELBO (-6.153998) evaluations: (101) 
-#> Path [6] :Initial log joint density = -6.752169 
+#>               5      -6.748e+00      1.997e-03   5.573e-05    1.000e+00  1.000e+00       126 -6.216e+00 -6.216e+00                   
+#> Path [5] :Best Iter: [5] ELBO (-6.216169) evaluations: (126) 
+#> Path [6] :Initial log joint density = -7.472192 
 #> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      6.431e-04   9.017e-06    9.943e-01  9.943e-01        76 -6.254e+00 -6.254e+00                   
-#> Path [6] :Best Iter: [2] ELBO (-6.219041) evaluations: (76) 
-#> Path [7] :Initial log joint density = -6.964028 
+#>               5      -6.748e+00      1.059e-04   4.145e-07    1.000e+00  1.000e+00       126 -6.137e+00 -6.137e+00                   
+#> Path [6] :Best Iter: [5] ELBO (-6.137426) evaluations: (126) 
+#> Path [7] :Initial log joint density = -8.723559 
 #> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      3.720e-04   7.938e-05    9.165e-01  9.165e-01       101 -6.157e+00 -6.157e+00                   
-#> Path [7] :Best Iter: [2] ELBO (-6.124107) evaluations: (101) 
-#> Path [8] :Initial log joint density = -7.712025 
+#>               5      -6.748e+00      3.317e-04   2.693e-06    1.000e+00  1.000e+00       126 -6.210e+00 -6.210e+00                   
+#> Path [7] :Best Iter: [3] ELBO (-6.175877) evaluations: (126) 
+#> Path [8] :Initial log joint density = -9.460464 
 #> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.616e-04   8.207e-07    1.000e+00  1.000e+00       126 -6.193e+00 -6.193e+00                   
-#> Path [8] :Best Iter: [2] ELBO (-6.154633) evaluations: (126) 
-#> Path [9] :Initial log joint density = -8.980186 
+#>               5      -6.748e+00      4.598e-04   4.575e-06    1.000e+00  1.000e+00       126 -6.241e+00 -6.241e+00                   
+#> Path [8] :Best Iter: [2] ELBO (-6.228285) evaluations: (126) 
+#> Path [9] :Initial log joint density = -18.781825 
 #> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      3.446e-04   2.899e-06    1.000e+00  1.000e+00       126 -6.172e+00 -6.172e+00                   
-#> Path [9] :Best Iter: [5] ELBO (-6.172384) evaluations: (126) 
-#> Path [10] :Initial log joint density = -6.762839 
+#>               5      -6.748e+00      4.081e-04   6.355e-06    1.000e+00  1.000e+00       126 -6.256e+00 -6.256e+00                   
+#> Path [9] :Best Iter: [2] ELBO (-6.178423) evaluations: (126) 
+#> Path [10] :Initial log joint density = -7.492978 
 #> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      1.503e-03   8.368e-06    9.796e-01  9.796e-01        76 -6.234e+00 -6.234e+00                   
-#> Path [10] :Best Iter: [2] ELBO (-6.202358) evaluations: (76) 
-#> Total log probability function evaluations:1210 
-#> Pareto k value (0.74) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. 
+#>               5      -6.748e+00      8.830e-04   2.382e-06    1.000e+00  1.000e+00       126 -6.191e+00 -6.191e+00                   
+#> Path [10] :Best Iter: [5] ELBO (-6.191391) evaluations: (126) 
+#> Total log probability function evaluations:1410 
 #> Finished in  0.1 seconds.
 
 # Specifying initial values as a function
diff --git a/docs/reference/CmdStanPathfinder.html b/docs/reference/CmdStanPathfinder.html
index 1b511142b..b25331a05 100644
--- a/docs/reference/CmdStanPathfinder.html
+++ b/docs/reference/CmdStanPathfinder.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/CmdStanVB.html b/docs/reference/CmdStanVB.html
index 86708be79..b6e635111 100644
--- a/docs/reference/CmdStanVB.html
+++ b/docs/reference/CmdStanVB.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/as_draws.CmdStanMCMC.html b/docs/reference/as_draws.CmdStanMCMC.html
index b04631b25..691170a8b 100644
--- a/docs/reference/as_draws.CmdStanMCMC.html
+++ b/docs/reference/as_draws.CmdStanMCMC.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -158,41 +158,41 @@ 
Examples

 #> 
 #>          chain
 #> iteration   1   2   3   4
-#>         1 -65 -67 -65 -66
-#>         2 -66 -65 -65 -67
-#>         3 -65 -65 -65 -65
-#>         4 -65 -66 -65 -65
-#>         5 -66 -69 -71 -66
+#>         1 -66 -65 -65 -64
+#>         2 -67 -67 -65 -68
+#>         3 -66 -65 -65 -66
+#>         4 -65 -66 -64 -66
+#>         5 -64 -67 -65 -66
 #> 
 #> , , variable = alpha
 #> 
 #>          chain
 #> iteration     1    2    3    4
-#>         1 0.448 0.19 0.36 0.34
-#>         2 0.343 0.57 0.28 0.53
-#>         3 0.441 0.54 0.56 0.45
-#>         4 0.095 0.75 0.19 0.57
-#>         5 0.089 0.81 0.57 0.20
+#>         1 0.792 0.30 0.38 0.38
+#>         2 0.061 0.54 0.49 0.14
+#>         3 0.538 0.23 0.47 0.29
+#>         4 0.446 0.10 0.37 0.41
+#>         5 0.253 0.68 0.46 0.56
 #> 
 #> , , variable = beta[1]
 #> 
 #>          chain
 #> iteration     1     2     3     4
-#>         1 -0.40 -0.13 -0.86 -0.87
-#>         2 -0.60 -0.75 -0.39 -0.66
-#>         3 -0.46 -1.00 -0.90 -0.61
-#>         4 -0.74 -0.72 -0.90 -0.87
-#>         5 -0.45 -0.80 -0.71 -0.99
+#>         1 -0.93 -0.46 -0.99 -0.75
+#>         2 -0.60 -0.87 -0.93 -0.84
+#>         3 -0.67 -0.51 -0.45 -0.41
+#>         4 -0.90 -0.78 -0.75 -0.19
+#>         5 -0.63 -0.57 -0.45 -0.62
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration     1     2      3     4
-#>         1 -0.29 -0.25 -0.041 -0.62
-#>         2 -0.70 -0.35 -0.376  0.26
-#>         3 -0.12 -0.21 -0.190 -0.23
-#>         4 -0.48 -0.34 -0.117 -0.20
-#>         5 -0.44 -0.10  0.289 -0.28
+#> iteration     1      2     3      4
+#>         1 -0.26  0.031 -0.15 -0.195
+#>         2  0.12 -0.226 -0.36 -0.386
+#>         3 -0.02 -0.199 -0.15 -0.548
+#>         4 -0.31 -0.013 -0.20 -0.084
+#>         5 -0.12 -0.615 -0.09 -0.610
 #> 
 #> # ... with 995 more iterations, and 101 more variables
 
@@ -203,67 +203,68 @@ 
Examples

 #> [1] -66 ± 1.5 
 #> 
 #> $alpha: rvar<1000,4>[1] mean ± sd:
-#> [1] 0.38 ± 0.22 
+#> [1] 0.37 ± 0.22 
 #> 
 #> $beta: rvar<1000,4>[3] mean ± sd:
 #> [1] -0.67 ± 0.25  -0.28 ± 0.23   0.69 ± 0.27 
 #> 
 #> $log_lik: rvar<1000,4>[100] mean ± sd:
-#>   [1] -0.517 ± 0.101  -0.399 ± 0.145  -0.501 ± 0.225  -0.446 ± 0.153 
-#>   [5] -1.185 ± 0.288  -0.589 ± 0.186  -0.639 ± 0.122  -0.277 ± 0.138 
-#>   [9] -0.692 ± 0.164  -0.743 ± 0.239  -0.278 ± 0.126  -0.492 ± 0.239 
-#>  [13] -0.656 ± 0.204  -0.362 ± 0.176  -0.278 ± 0.108  -0.274 ± 0.089 
-#>  [17] -1.598 ± 0.297  -0.478 ± 0.109  -0.232 ± 0.077  -0.113 ± 0.080 
-#>  [21] -0.211 ± 0.089  -0.567 ± 0.147  -0.329 ± 0.142  -0.136 ± 0.067 
-#>  [25] -0.452 ± 0.119  -1.523 ± 0.345  -0.305 ± 0.123  -0.445 ± 0.085 
-#>  [29] -0.723 ± 0.230  -0.694 ± 0.192  -0.485 ± 0.161  -0.423 ± 0.108 
-#>  [33] -0.407 ± 0.124  -0.063 ± 0.050  -0.584 ± 0.183  -0.324 ± 0.134 
-#>  [37] -0.702 ± 0.231  -0.309 ± 0.148  -0.179 ± 0.111  -0.681 ± 0.127 
-#>  [41] -1.135 ± 0.256  -0.932 ± 0.194  -0.410 ± 0.262  -1.177 ± 0.192 
-#>  [45] -0.359 ± 0.120  -0.580 ± 0.127  -0.301 ± 0.127  -0.324 ± 0.084 
-#>  [49] -0.318 ± 0.082  -1.290 ± 0.332  -0.287 ± 0.095  -0.833 ± 0.145 
-#>  [53] -0.400 ± 0.130  -0.372 ± 0.143  -0.382 ± 0.133  -0.319 ± 0.194 
-#>  [57] -0.658 ± 0.119  -0.953 ± 0.354  -1.365 ± 0.337  -0.978 ± 0.163 
-#>  [61] -0.541 ± 0.099  -0.872 ± 0.313  -0.116 ± 0.075  -0.903 ± 0.248 
-#>  [65] -2.029 ± 0.603  -0.508 ± 0.136  -0.276 ± 0.083  -1.064 ± 0.237 
-#>  [69] -0.435 ± 0.086  -0.639 ± 0.237  -0.609 ± 0.205  -0.461 ± 0.167 
-#>  [73] -1.492 ± 0.362  -0.948 ± 0.200  -1.139 ± 0.388  -0.374 ± 0.142 
-#>  [77] -0.878 ± 0.139  -0.489 ± 0.172  -0.766 ± 0.193  -0.539 ± 0.194 
-#>  [81] -0.161 ± 0.098  -0.220 ± 0.134  -0.343 ± 0.083  -0.275 ± 0.093 
-#>  [85] -0.130 ± 0.076  -1.131 ± 0.318  -0.823 ± 0.128  -0.777 ± 0.238 
-#>  [89] -1.284 ± 0.315  -0.258 ± 0.133  -0.384 ± 0.127  -1.503 ± 0.353 
-#>  [93] -0.736 ± 0.221  -0.318 ± 0.090  -0.387 ± 0.113  -1.578 ± 0.293 
-#>  [97] -0.431 ± 0.102  -1.055 ± 0.371  -0.693 ± 0.139  -0.391 ± 0.098 
+#>   [1] -0.518 ± 0.101  -0.398 ± 0.148  -0.500 ± 0.222  -0.445 ± 0.153 
+#>   [5] -1.183 ± 0.289  -0.593 ± 0.195  -0.637 ± 0.127  -0.278 ± 0.133 
+#>   [9] -0.697 ± 0.172  -0.743 ± 0.237  -0.280 ± 0.126  -0.492 ± 0.240 
+#>  [13] -0.655 ± 0.213  -0.361 ± 0.174  -0.279 ± 0.109  -0.275 ± 0.087 
+#>  [17] -1.594 ± 0.288  -0.481 ± 0.109  -0.232 ± 0.075  -0.112 ± 0.078 
+#>  [21] -0.211 ± 0.087  -0.571 ± 0.151  -0.330 ± 0.138  -0.135 ± 0.065 
+#>  [25] -0.451 ± 0.123  -1.520 ± 0.340  -0.307 ± 0.123  -0.446 ± 0.086 
+#>  [29] -0.722 ± 0.230  -0.699 ± 0.193  -0.489 ± 0.165  -0.425 ± 0.111 
+#>  [33] -0.406 ± 0.128  -0.062 ± 0.048  -0.583 ± 0.188  -0.325 ± 0.130 
+#>  [37] -0.702 ± 0.230  -0.309 ± 0.149  -0.178 ± 0.110  -0.684 ± 0.132 
+#>  [41] -1.131 ± 0.261  -0.937 ± 0.201  -0.413 ± 0.266  -1.175 ± 0.188 
+#>  [45] -0.359 ± 0.118  -0.578 ± 0.131  -0.301 ± 0.128  -0.324 ± 0.083 
+#>  [49] -0.319 ± 0.081  -1.288 ± 0.331  -0.288 ± 0.094  -0.832 ± 0.146 
+#>  [53] -0.402 ± 0.132  -0.371 ± 0.142  -0.381 ± 0.136  -0.320 ± 0.188 
+#>  [57] -0.660 ± 0.121  -0.954 ± 0.356  -1.371 ± 0.345  -0.976 ± 0.161 
+#>  [61] -0.543 ± 0.100  -0.872 ± 0.317  -0.115 ± 0.071  -0.899 ± 0.250 
+#>  [65] -2.024 ± 0.609  -0.509 ± 0.139  -0.276 ± 0.081  -1.059 ± 0.239 
+#>  [69] -0.437 ± 0.086  -0.642 ± 0.235  -0.608 ± 0.213  -0.460 ± 0.173 
+#>  [73] -1.496 ± 0.368  -0.947 ± 0.199  -1.139 ± 0.392  -0.373 ± 0.140 
+#>  [77] -0.876 ± 0.143  -0.490 ± 0.174  -0.767 ± 0.193  -0.537 ± 0.197 
+#>  [81] -0.160 ± 0.100  -0.220 ± 0.138  -0.344 ± 0.082  -0.275 ± 0.092 
+#>  [85] -0.129 ± 0.074  -1.136 ± 0.323  -0.821 ± 0.130  -0.776 ± 0.248 
+#>  [89] -1.289 ± 0.322  -0.258 ± 0.136  -0.383 ± 0.131  -1.501 ± 0.351 
+#>  [93] -0.736 ± 0.220  -0.318 ± 0.088  -0.389 ± 0.113  -1.575 ± 0.284 
+#>  [97] -0.432 ± 0.101  -1.058 ± 0.374  -0.690 ± 0.144  -0.392 ± 0.098 
 #> 
 posterior::as_draws_list(fit)
 #> # A draws_list: 1000 iterations, 4 chains, and 105 variables
 #> 
 #> [chain = 1]
 #> $lp__
-#>  [1] -65 -66 -65 -65 -66 -66 -65 -64 -65 -65
+#>  [1] -66 -67 -66 -65 -64 -65 -65 -66 -64 -66
 #> 
 #> $alpha
-#>  [1] 0.448 0.343 0.441 0.095 0.089 0.680 0.373 0.327 0.441 0.215
+#>  [1] 0.792 0.061 0.538 0.446 0.253 0.343 0.387 0.827 0.506 0.713
 #> 
 #> $`beta[1]`
-#>  [1] -0.40 -0.60 -0.46 -0.74 -0.45 -0.80 -0.65 -0.85 -0.39 -0.36
+#>  [1] -0.93 -0.60 -0.67 -0.90 -0.63 -0.71 -0.64 -0.60 -0.73 -0.96
 #> 
 #> $`beta[2]`
-#>  [1] -0.29 -0.70 -0.12 -0.48 -0.44 -0.14 -0.50 -0.33 -0.25 -0.33
+#>  [1] -0.26  0.12 -0.02 -0.31 -0.12 -0.46 -0.44 -0.39 -0.32 -0.22
 #> 
 #> 
 #> [chain = 2]
 #> $lp__
-#>  [1] -67 -65 -65 -66 -69 -68 -67 -67 -68 -69
+#>  [1] -65 -67 -65 -66 -67 -67 -67 -67 -67 -72
 #> 
 #> $alpha
-#>  [1]  0.186  0.574  0.536  0.753  0.813 -0.010  0.027  0.027  0.628  0.271
+#>  [1]  0.303  0.538  0.227  0.104  0.678  0.596 -0.041  0.769  0.340  0.948
 #> 
 #> $`beta[1]`
-#>  [1] -0.126 -0.754 -1.000 -0.716 -0.801 -0.503 -0.628 -0.628 -0.985 -0.052
+#>  [1] -0.46 -0.87 -0.51 -0.78 -0.57 -0.50 -0.57 -0.67 -0.60 -0.47
 #> 
 #> $`beta[2]`
-#>  [1] -0.25 -0.35 -0.21 -0.34 -0.10 -0.34 -0.17 -0.17 -0.70 -0.21
+#>  [1]  0.03060 -0.22607 -0.19885 -0.01303 -0.61503 -0.63211  0.00036 -0.41151
+#>  [9] -0.46209 -0.88087
 #> 
 #> # ... with 990 more iterations, and 2 more chains, and 101 more variables
 # }
diff --git a/docs/reference/as_mcmc.list.html b/docs/reference/as_mcmc.list.html
index 6d0e719b5..222a49f3d 100644
--- a/docs/reference/as_mcmc.list.html
+++ b/docs/reference/as_mcmc.list.html
@@ -23,7 +23,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/cmdstan_coercion.html b/docs/reference/cmdstan_coercion.html
index f0de95037..c1af8993c 100644
--- a/docs/reference/cmdstan_coercion.html
+++ b/docs/reference/cmdstan_coercion.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/cmdstan_default_install_path.html b/docs/reference/cmdstan_default_install_path.html
index d8ecd6d6a..e4039b788 100644
--- a/docs/reference/cmdstan_default_install_path.html
+++ b/docs/reference/cmdstan_default_install_path.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/cmdstan_default_path.html b/docs/reference/cmdstan_default_path.html
index 826c04c68..7f87720a5 100644
--- a/docs/reference/cmdstan_default_path.html
+++ b/docs/reference/cmdstan_default_path.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
diff --git a/docs/reference/cmdstan_model-1.png b/docs/reference/cmdstan_model-1.png
index aa185638c..35206a7af 100644
Binary files a/docs/reference/cmdstan_model-1.png and b/docs/reference/cmdstan_model-1.png differ
diff --git a/docs/reference/cmdstan_model-2.png b/docs/reference/cmdstan_model-2.png
index 4cf81fa80..b078570c6 100644
Binary files a/docs/reference/cmdstan_model-2.png and b/docs/reference/cmdstan_model-2.png differ
diff --git a/docs/reference/cmdstan_model-3.png b/docs/reference/cmdstan_model-3.png
index c67b37ed1..35367ed42 100644
Binary files a/docs/reference/cmdstan_model-3.png and b/docs/reference/cmdstan_model-3.png differ
diff --git a/docs/reference/cmdstan_model.html b/docs/reference/cmdstan_model.html
index feabb6046..f3966bc19 100644
--- a/docs/reference/cmdstan_model.html
+++ b/docs/reference/cmdstan_model.html
@@ -23,7 +23,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -185,7 +185,7 @@ 
Examples

 # Otherwise use the `path` argument to specify the location of your
 # CmdStan installation.)
 set_cmdstan_path(path = NULL)
-#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0
+#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0
 
 # Create a CmdStanModel object from a Stan program,
 # here using the example model that comes with CmdStan
@@ -287,8 +287,8 @@ 
Examples

 #> # A tibble: 2 × 10
 #>   variable   mean median    sd   mad      q5    q95  rhat ess_bulk ess_tail
 #>   <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
-#> 1 lp__     -7.30  -7.00  0.811 0.344 -8.83   -6.75   1.00     702.     776.
-#> 2 theta     0.254  0.238 0.125 0.124  0.0807  0.483  1.00     634.     580.
+#> 1 lp__     -7.35  -7.01  0.882 0.353 -9.14   -6.75   1.00     724.     896.
+#> 2 theta     0.254  0.239 0.129 0.126  0.0737  0.488  1.00     532.     657.
 
 # Check sampling diagnostics
 fit_mcmc$diagnostic_summary()
@@ -299,7 +299,7 @@ 
Examples

 #> [1] 0 0
 #> 
 #> $ebfmi
-#> [1] 1.1148699 0.8012192
+#> [1] 1.1148479 0.7568734
 #> 
 
 # Get posterior draws
@@ -309,22 +309,22 @@ 
Examples

 #> , , variable = lp__
 #> 
 #>          chain
-#> iteration    1     2
-#>         1 -7.0  -6.8
-#>         2 -7.9  -6.9
-#>         3 -7.4  -6.8
-#>         4 -6.7  -6.8
-#>         5 -6.9 -10.2
+#> iteration    1    2
+#>         1 -7.0 -8.1
+#>         2 -7.9 -7.9
+#>         3 -7.4 -7.0
+#>         4 -6.7 -6.8
+#>         5 -6.9 -6.8
 #> 
 #> , , variable = theta
 #> 
 #>          chain
-#> iteration    1    2
-#>         1 0.17 0.23
-#>         2 0.46 0.18
-#>         3 0.41 0.28
-#>         4 0.25 0.23
-#>         5 0.18 0.62
+#> iteration    1     2
+#>         1 0.17 0.088
+#>         2 0.46 0.097
+#>         3 0.41 0.167
+#>         4 0.25 0.292
+#>         5 0.18 0.238
 #> 
 #> # ... with 995 more iterations
 
@@ -350,11 +350,6 @@ 
Examples

 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
-# For models fit using MCMC, if you like working with RStan's stanfit objects
-# then you can create one with rstan::read_stan_csv()
-# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
-
-
 # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
 # and also demonstrate specifying data as a path to a file instead of a list
 my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
@@ -364,7 +359,7 @@ 
Examples

 #>        6      -5.00402   0.000246518   8.73164e-07           1           1        9    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_optim$summary()
 #> # A tibble: 2 × 2
 #>   variable estimate
@@ -375,12 +370,12 @@ 
Examples

 # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -13.9671 
+#> Initial log joint probability = -7.19041 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        5      -6.74802    0.00195337   5.29486e-05           1           1        8    
+#>        5      -6.74802   0.000219546   2.02164e-07           1           1        8    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
 #> Calculating Hessian 
 #> Calculating inverse of Cholesky factor 
@@ -405,14 +400,14 @@ 
Examples

 #> iteration: 1700 
 #> iteration: 1800 
 #> iteration: 1900 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
 #>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
-#> 1 lp__        -7.21  -6.97  0.646 0.293 -8.55  -6.75   
-#> 2 lp_approx__ -0.466 -0.220 0.655 0.297 -1.78  -0.00205
-#> 3 theta        0.270  0.250 0.120 0.121  0.109  0.497  
+#> 1 lp__        -7.23  -6.98  0.662 0.312 -8.57  -6.75   
+#> 2 lp_approx__ -0.490 -0.230 0.676 0.315 -1.92  -0.00147
+#> 3 theta        0.269  0.251 0.121 0.123  0.101  0.490  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -421,8 +416,8 @@ 
Examples

 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 2.3e-05 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds. 
+#> Gradient evaluation took 9e-06 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -438,7 +433,7 @@ 
Examples

 #>    300           -6.186             0.339            0.010   MEDIAN ELBO CONVERGED 
 #> Drawing a sample of size 1000 from the approximate posterior...  
 #> COMPLETED. 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_vb$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
@@ -469,7 +464,7 @@ 
Examples

 #>               5      -6.748e+00      2.145e-04   1.301e-06    1.000e+00  1.000e+00       126 -6.197e+00 -6.197e+00                   
 #> Path [4] :Best Iter: [5] ELBO (-6.197118) evaluations: (126) 
 #> Total log probability function evaluations:4379 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_pf$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5    q95
@@ -486,48 +481,48 @@ 
Examples

 fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
                          history_size=50, max_lbfgs_iters=100)
 #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. 
-#> Path [1] :Initial log joint density = -7.405417 
+#> Path [1] :Initial log joint density = -19.520956 
 #> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      9.081e-05   3.233e-07    1.000e+00  1.000e+00       126 -6.231e+00 -6.231e+00                   
-#> Path [1] :Best Iter: [3] ELBO (-6.194155) evaluations: (126) 
-#> Path [2] :Initial log joint density = -7.706381 
+#>               4      -6.748e+00      1.226e-02   1.762e-04    1.000e+00  1.000e+00       101 -6.253e+00 -6.253e+00                   
+#> Path [1] :Best Iter: [2] ELBO (-6.195755) evaluations: (101) 
+#> Path [2] :Initial log joint density = -7.254863 
 #> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.603e-04   8.100e-07    1.000e+00  1.000e+00       126 -6.209e+00 -6.209e+00                   
-#> Path [2] :Best Iter: [5] ELBO (-6.209214) evaluations: (126) 
-#> Path [3] :Initial log joint density = -8.140103 
+#>               5      -6.748e+00      3.135e-04   3.796e-07    1.000e+00  1.000e+00       126 -6.215e+00 -6.215e+00                   
+#> Path [2] :Best Iter: [4] ELBO (-6.093719) evaluations: (126) 
+#> Path [3] :Initial log joint density = -9.357651 
 #> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      2.528e-04   1.703e-06    1.000e+00  1.000e+00       126 -6.231e+00 -6.231e+00                   
-#> Path [3] :Best Iter: [3] ELBO (-6.213740) evaluations: (126) 
-#> Path [4] :Initial log joint density = -6.779208 
+#>               5      -6.748e+00      4.272e-04   4.080e-06    1.000e+00  1.000e+00       126 -6.228e+00 -6.228e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.197602) evaluations: (126) 
+#> Path [4] :Initial log joint density = -15.654513 
 #> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      1.406e-04   3.696e-07    1.000e+00  1.000e+00       101 -6.183e+00 -6.183e+00                   
-#> Path [4] :Best Iter: [4] ELBO (-6.183257) evaluations: (101) 
-#> Path [5] :Initial log joint density = -6.762413 
+#>               5      -6.748e+00      1.955e-03   5.901e-05    1.000e+00  1.000e+00       126 -6.224e+00 -6.224e+00                   
+#> Path [4] :Best Iter: [5] ELBO (-6.224416) evaluations: (126) 
+#> Path [5] :Initial log joint density = -6.893187 
 #> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      2.209e-03   1.061e-04    1.000e+00  1.000e+00        76 -6.247e+00 -6.247e+00                   
-#> Path [5] :Best Iter: [2] ELBO (-6.191386) evaluations: (76) 
-#> Path [6] :Initial log joint density = -7.106649 
+#>               4      -6.748e+00      1.780e-04   2.911e-05    9.344e-01  9.344e-01       101 -6.239e+00 -6.239e+00                   
+#> Path [5] :Best Iter: [3] ELBO (-6.215896) evaluations: (101) 
+#> Path [6] :Initial log joint density = -10.329090 
 #> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      3.282e-03   7.049e-05    1.000e+00  1.000e+00       101 -6.179e+00 -6.179e+00                   
-#> Path [6] :Best Iter: [3] ELBO (-6.136787) evaluations: (101) 
-#> Path [7] :Initial log joint density = -7.037942 
+#>               5      -6.748e+00      7.385e-04   9.583e-06    1.000e+00  1.000e+00       126 -6.222e+00 -6.222e+00                   
+#> Path [6] :Best Iter: [3] ELBO (-6.212303) evaluations: (126) 
+#> Path [7] :Initial log joint density = -15.872224 
 #> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      2.570e-03   4.694e-05    1.000e+00  1.000e+00       101 -6.238e+00 -6.238e+00                   
-#> Path [7] :Best Iter: [3] ELBO (-6.227629) evaluations: (101) 
-#> Path [8] :Initial log joint density = -6.772444 
+#>               5      -6.748e+00      1.903e-03   5.733e-05    1.000e+00  1.000e+00       126 -6.254e+00 -6.254e+00                   
+#> Path [7] :Best Iter: [4] ELBO (-6.195869) evaluations: (126) 
+#> Path [8] :Initial log joint density = -10.958418 
 #> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               4      -6.748e+00      9.980e-05   2.088e-07    1.000e+00  1.000e+00       101 -6.208e+00 -6.208e+00                   
-#> Path [8] :Best Iter: [2] ELBO (-6.208417) evaluations: (101) 
-#> Path [9] :Initial log joint density = -17.488355 
+#>               5      -6.748e+00      9.246e-04   1.360e-05    1.000e+00  1.000e+00       126 -6.214e+00 -6.214e+00                   
+#> Path [8] :Best Iter: [2] ELBO (-6.186854) evaluations: (126) 
+#> Path [9] :Initial log joint density = -12.861849 
 #> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.187e-03   3.028e-05    1.000e+00  1.000e+00       126 -6.283e+00 -6.283e+00                   
-#> Path [9] :Best Iter: [2] ELBO (-6.205015) evaluations: (126) 
-#> Path [10] :Initial log joint density = -7.447696 
+#>               5      -6.748e+00      1.621e-03   3.641e-05    1.000e+00  1.000e+00       126 -6.213e+00 -6.213e+00                   
+#> Path [9] :Best Iter: [5] ELBO (-6.213458) evaluations: (126) 
+#> Path [10] :Initial log joint density = -16.360860 
 #> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.004e-04   3.798e-07    1.000e+00  1.000e+00       126 -6.264e+00 -6.264e+00                   
-#> Path [10] :Best Iter: [3] ELBO (-6.226969) evaluations: (126) 
-#> Total log probability function evaluations:1260 
-#> Finished in  0.2 seconds.
+#>               5      -6.748e+00      1.745e-03   5.153e-05    1.000e+00  1.000e+00       126 -6.169e+00 -6.169e+00                   
+#> Path [10] :Best Iter: [5] ELBO (-6.169190) evaluations: (126) 
+#> Total log probability function evaluations:1360 
+#> Finished in  0.1 seconds.
 
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
@@ -609,7 +604,7 @@ 
Examples

 #>        6      -5.00402   0.000237915   9.55309e-07           1           1        9    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_optim_w_init_list$init()
 #> [[1]]
 #> [[1]]$theta
diff --git a/docs/reference/cmdstanr-package-1.png b/docs/reference/cmdstanr-package-1.png
index aa185638c..35206a7af 100644
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diff --git a/docs/reference/cmdstanr-package-3.png b/docs/reference/cmdstanr-package-3.png
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diff --git a/docs/reference/cmdstanr-package.html b/docs/reference/cmdstanr-package.html
index 497703b7b..a3d781a32 100644
--- a/docs/reference/cmdstanr-package.html
+++ b/docs/reference/cmdstanr-package.html
@@ -19,7 +19,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -211,7 +211,7 @@ 
Examples

 # Otherwise use the `path` argument to specify the location of your
 # CmdStan installation.)
 set_cmdstan_path(path = NULL)
-#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0
+#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0
 
 # Create a CmdStanModel object from a Stan program,
 # here using the example model that comes with CmdStan
@@ -313,8 +313,8 @@ 
Examples

 #> # A tibble: 2 × 10
 #>   variable   mean median    sd   mad      q5    q95  rhat ess_bulk ess_tail
 #>   <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
-#> 1 lp__     -7.30  -7.00  0.811 0.344 -8.83   -6.75   1.00     702.     776.
-#> 2 theta     0.254  0.238 0.125 0.124  0.0807  0.483  1.00     634.     580.
+#> 1 lp__     -7.35  -7.01  0.882 0.353 -9.14   -6.75   1.00     724.     896.
+#> 2 theta     0.254  0.239 0.129 0.126  0.0737  0.488  1.00     532.     657.
 
 # Check sampling diagnostics
 fit_mcmc$diagnostic_summary()
@@ -325,7 +325,7 @@ 
Examples

 #> [1] 0 0
 #> 
 #> $ebfmi
-#> [1] 1.1148699 0.8012192
+#> [1] 1.1148479 0.7568734
 #> 
 
 # Get posterior draws
@@ -335,22 +335,22 @@ 
Examples

 #> , , variable = lp__
 #> 
 #>          chain
-#> iteration    1     2
-#>         1 -7.0  -6.8
-#>         2 -7.9  -6.9
-#>         3 -7.4  -6.8
-#>         4 -6.7  -6.8
-#>         5 -6.9 -10.2
+#> iteration    1    2
+#>         1 -7.0 -8.1
+#>         2 -7.9 -7.9
+#>         3 -7.4 -7.0
+#>         4 -6.7 -6.8
+#>         5 -6.9 -6.8
 #> 
 #> , , variable = theta
 #> 
 #>          chain
-#> iteration    1    2
-#>         1 0.17 0.23
-#>         2 0.46 0.18
-#>         3 0.41 0.28
-#>         4 0.25 0.23
-#>         5 0.18 0.62
+#> iteration    1     2
+#>         1 0.17 0.088
+#>         2 0.46 0.097
+#>         3 0.41 0.167
+#>         4 0.25 0.292
+#>         5 0.18 0.238
 #> 
 #> # ... with 995 more iterations
 
@@ -376,11 +376,6 @@ 
Examples

 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
 
 
-# For models fit using MCMC, if you like working with RStan's stanfit objects
-# then you can create one with rstan::read_stan_csv()
-# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
-
-
 # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
 # and also demonstrate specifying data as a path to a file instead of a list
 my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
@@ -390,7 +385,7 @@ 
Examples

 #>        6      -5.00402   0.000246518   8.73164e-07           1           1        9    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_optim$summary()
 #> # A tibble: 2 × 2
 #>   variable estimate
@@ -401,12 +396,12 @@ 
Examples

 # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
 # to the posterior
 fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
-#> Initial log joint probability = -7.37731 
+#> Initial log joint probability = -6.80195 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>        5      -6.74802   8.45677e-05   2.88228e-07           1           1        8    
+#>        4      -6.74802    0.00029907   1.30133e-06           1           1        7    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
 #> Calculating Hessian 
 #> Calculating inverse of Cholesky factor 
@@ -431,14 +426,14 @@ 
Examples

 #> iteration: 1700 
 #> iteration: 1800 
 #> iteration: 1900 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_laplace$summary()
 #> # A tibble: 3 × 7
-#>   variable      mean median    sd   mad      q5      q95
-#>   <chr>        <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl>
-#> 1 lp__        -7.25  -6.98  0.745 0.324 -8.71   -6.75   
-#> 2 lp_approx__ -0.518 -0.231 0.764 0.321 -1.95   -0.00198
-#> 3 theta        0.266  0.247 0.124 0.121  0.0979  0.501  
+#>   variable      mean median    sd   mad     q5      q95
+#>   <chr>        <dbl>  <dbl> <dbl> <dbl>  <dbl>    <dbl>
+#> 1 lp__        -7.22  -6.96  0.660 0.293 -8.56  -6.75   
+#> 2 lp_approx__ -0.479 -0.220 0.648 0.296 -1.85  -0.00230
+#> 3 theta        0.271  0.251 0.122 0.119  0.101  0.501  
 
 # Run 'variational' method to use ADVI to approximate posterior
 fit_vb <- mod$variational(data = stan_data, seed = 123)
@@ -447,8 +442,8 @@ 
Examples

 #>   This procedure has not been thoroughly tested and may be unstable 
 #>   or buggy. The interface is subject to change. 
 #> ------------------------------------------------------------ 
-#> Gradient evaluation took 1.5e-05 seconds 
-#> 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds. 
+#> Gradient evaluation took 1e-05 seconds 
+#> 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds. 
 #> Adjust your expectations accordingly! 
 #> Begin eta adaptation. 
 #> Iteration:   1 / 250 [  0%]  (Adaptation) 
@@ -464,7 +459,7 @@ 
Examples

 #>    300           -6.186             0.339            0.010   MEDIAN ELBO CONVERGED 
 #> Drawing a sample of size 1000 from the approximate posterior...  
 #> COMPLETED. 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_vb$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad     q5      q95
@@ -495,7 +490,7 @@ 
Examples

 #>               5      -6.748e+00      2.145e-04   1.301e-06    1.000e+00  1.000e+00       126 -6.197e+00 -6.197e+00                   
 #> Path [4] :Best Iter: [5] ELBO (-6.197118) evaluations: (126) 
 #> Total log probability function evaluations:4379 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_pf$summary()
 #> # A tibble: 3 × 7
 #>   variable      mean median    sd   mad      q5    q95
@@ -512,49 +507,48 @@ 
Examples

 fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
                          history_size=50, max_lbfgs_iters=100)
 #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. 
-#> Path [1] :Initial log joint density = -15.640416 
+#> Path [1] :Initial log joint density = -8.288264 
 #> Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.958e-03   5.910e-05    1.000e+00  1.000e+00       126 -6.252e+00 -6.252e+00                   
-#> Path [1] :Best Iter: [4] ELBO (-6.217832) evaluations: (126) 
-#> Path [2] :Initial log joint density = -7.289736 
+#>               5      -6.748e+00      2.788e-04   2.003e-06    1.000e+00  1.000e+00       126 -6.242e+00 -6.242e+00                   
+#> Path [1] :Best Iter: [2] ELBO (-6.189407) evaluations: (126) 
+#> Path [2] :Initial log joint density = -7.122559 
 #> Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      3.739e-04   5.183e-07    1.000e+00  1.000e+00       126 -6.223e+00 -6.223e+00                   
-#> Path [2] :Best Iter: [4] ELBO (-6.180764) evaluations: (126) 
-#> Path [3] :Initial log joint density = -6.814578 
+#>               4      -6.748e+00      3.446e-03   7.646e-05    1.000e+00  1.000e+00       101 -6.267e+00 -6.267e+00                   
+#> Path [2] :Best Iter: [2] ELBO (-6.217814) evaluations: (101) 
+#> Path [3] :Initial log joint density = -6.904847 
 #> Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      4.711e-03   8.907e-05    9.425e-01  9.425e-01        76 -6.167e+00 -6.167e+00                   
-#> Path [3] :Best Iter: [3] ELBO (-6.166803) evaluations: (76) 
-#> Path [4] :Initial log joint density = -6.750524 
+#>               4      -6.748e+00      1.217e-03   1.351e-05    1.000e+00  1.000e+00       101 -6.256e+00 -6.256e+00                   
+#> Path [3] :Best Iter: [3] ELBO (-6.226443) evaluations: (101) 
+#> Path [4] :Initial log joint density = -15.810600 
 #> Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      4.513e-04   7.212e-06    9.979e-01  9.979e-01        76 -6.172e+00 -6.172e+00                   
-#> Path [4] :Best Iter: [3] ELBO (-6.171786) evaluations: (76) 
-#> Path [5] :Initial log joint density = -7.332655 
+#>               5      -6.748e+00      1.919e-03   5.787e-05    1.000e+00  1.000e+00       126 -6.202e+00 -6.202e+00                   
+#> Path [4] :Best Iter: [4] ELBO (-6.144907) evaluations: (126) 
+#> Path [5] :Initial log joint density = -7.535814 
 #> Path [5] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      4.580e-04   7.427e-07    1.000e+00  1.000e+00       126 -6.250e+00 -6.250e+00                   
-#> Path [5] :Best Iter: [3] ELBO (-6.236262) evaluations: (126) 
-#> Path [6] :Initial log joint density = -12.928546 
+#>               5      -6.748e+00      1.206e-04   5.112e-07    1.000e+00  1.000e+00       126 -6.233e+00 -6.233e+00                   
+#> Path [5] :Best Iter: [4] ELBO (-6.165244) evaluations: (126) 
+#> Path [6] :Initial log joint density = -12.317092 
 #> Path [6] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      1.646e-03   3.751e-05    1.000e+00  1.000e+00       126 -6.201e+00 -6.201e+00                   
-#> Path [6] :Best Iter: [5] ELBO (-6.201184) evaluations: (126) 
-#> Path [7] :Initial log joint density = -10.302489 
+#>               5      -6.748e+00      1.394e-03   2.744e-05    1.000e+00  1.000e+00       126 -6.247e+00 -6.247e+00                   
+#> Path [6] :Best Iter: [3] ELBO (-6.207884) evaluations: (126) 
+#> Path [7] :Initial log joint density = -6.970206 
 #> Path [7] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      7.301e-04   9.415e-06    1.000e+00  1.000e+00       126 -6.220e+00 -6.220e+00                   
-#> Path [7] :Best Iter: [3] ELBO (-6.177461) evaluations: (126) 
-#> Path [8] :Initial log joint density = -9.256093 
+#>               4      -6.748e+00      3.919e-04   8.523e-05    9.151e-01  9.151e-01       101 -6.187e+00 -6.187e+00                   
+#> Path [7] :Best Iter: [4] ELBO (-6.187348) evaluations: (101) 
+#> Path [8] :Initial log joint density = -6.960470 
 #> Path [8] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      3.955e-04   3.617e-06    1.000e+00  1.000e+00       126 -6.238e+00 -6.238e+00                   
-#> Path [8] :Best Iter: [3] ELBO (-6.220144) evaluations: (126) 
-#> Path [9] :Initial log joint density = -18.100368 
+#>               4      -6.748e+00      1.773e-03   2.529e-05    1.000e+00  1.000e+00       101 -6.148e+00 -6.148e+00                   
+#> Path [8] :Best Iter: [4] ELBO (-6.147740) evaluations: (101) 
+#> Path [9] :Initial log joint density = -6.771981 
 #> Path [9] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               5      -6.748e+00      8.150e-04   1.759e-05    1.000e+00  1.000e+00       126 -6.245e+00 -6.245e+00                   
-#> Path [9] :Best Iter: [2] ELBO (-6.200207) evaluations: (126) 
-#> Path [10] :Initial log joint density = -6.749568 
+#>               3      -6.748e+00      2.126e-03   4.338e-07    9.709e-01  9.709e-01        76 -6.239e+00 -6.239e+00                   
+#> Path [9] :Best Iter: [2] ELBO (-6.211440) evaluations: (76) 
+#> Path [10] :Initial log joint density = -7.099410 
 #> Path [10] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
-#>               3      -6.748e+00      2.564e-04   4.111e-06    1.000e+00  1.000e+00        76 -6.255e+00 -6.255e+00                   
-#> Path [10] :Best Iter: [2] ELBO (-6.165609) evaluations: (76) 
+#>               5      -6.748e+00      1.211e-04   7.078e-08    1.000e+00  1.000e+00       126 -6.227e+00 -6.227e+00                   
+#> Path [10] :Best Iter: [2] ELBO (-6.196731) evaluations: (126) 
 #> Total log probability function evaluations:1260 
-#> Pareto k value (0.89) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 
 # Specifying initial values as a function
 fit_mcmc_w_init_fun <- mod$sample(
@@ -636,7 +630,7 @@ 
Examples

 #>        6      -5.00402   0.000237915   9.55309e-07           1           1        9    
 #> Optimization terminated normally:  
 #>   Convergence detected: relative gradient magnitude is below tolerance 
-#> Finished in  0.2 seconds.
+#> Finished in  0.1 seconds.
 fit_optim_w_init_list$init()
 #> [[1]]
 #> [[1]]$theta
diff --git a/docs/reference/cmdstanr_example.html b/docs/reference/cmdstanr_example.html
index e844dcaaf..b4484a920 100644
--- a/docs/reference/cmdstanr_example.html
+++ b/docs/reference/cmdstanr_example.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -187,18 +187,18 @@ 
Examples

 fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2)
 fit_logistic_mcmc$summary()
 #> # A tibble: 105 × 10
-#>    variable      mean  median    sd    mad       q5     q95  rhat ess_bulk
-#>    <chr>        <dbl>   <dbl> <dbl>  <dbl>    <dbl>   <dbl> <dbl>    <dbl>
-#>  1 lp__       -65.9   -65.6   1.43  1.23   -68.7    -64.3    1.00     890.
-#>  2 alpha        0.377   0.375 0.220 0.220    0.0153   0.743  1.00    2007.
-#>  3 beta[1]     -0.670  -0.663 0.247 0.244   -1.10    -0.273  1.00    1880.
-#>  4 beta[2]     -0.268  -0.269 0.223 0.218   -0.637    0.106  1.00    1834.
-#>  5 beta[3]      0.684   0.681 0.271 0.271    0.243    1.14   1.00    2163.
-#>  6 log_lik[1]  -0.516  -0.511 0.101 0.0973  -0.694   -0.363  1.00    2030.
-#>  7 log_lik[2]  -0.398  -0.38  0.143 0.141   -0.661   -0.204  1.00    2280.
-#>  8 log_lik[3]  -0.493  -0.463 0.216 0.196   -0.883   -0.197  1.00    1914.
-#>  9 log_lik[4]  -0.450  -0.430 0.153 0.148   -0.730   -0.229  1.00    2150.
-#> 10 log_lik[5]  -1.19   -1.16  0.288 0.283   -1.70    -0.769  1.00    2194.
+#>    variable      mean  median     sd    mad       q5      q95  rhat ess_bulk
+#>    <chr>        <dbl>   <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
+#>  1 lp__       -66.0   -65.6   1.43   1.22   -68.8    -64.3     1.00     963.
+#>  2 alpha        0.376   0.373 0.220  0.221    0.0226   0.745   1.00    2237.
+#>  3 beta[1]     -0.671  -0.663 0.250  0.252   -1.10    -0.258   1.00    1930.
+#>  4 beta[2]     -0.262  -0.261 0.222  0.229   -0.629    0.0934  1.00    1801.
+#>  5 beta[3]      0.674   0.681 0.264  0.263    0.242    1.10    1.00    1979.
+#>  6 log_lik[1]  -0.515  -0.509 0.0991 0.0964  -0.681   -0.363   1.00    2100.
+#>  7 log_lik[2]  -0.401  -0.381 0.147  0.138   -0.671   -0.197   1.00    2010.
+#>  8 log_lik[3]  -0.489  -0.456 0.215  0.202   -0.891   -0.207   1.00    1914.
+#>  9 log_lik[4]  -0.455  -0.435 0.154  0.152   -0.726   -0.242   1.00    1918.
+#> 10 log_lik[5]  -1.18   -1.16  0.283  0.282   -1.69    -0.749   1.00    2470.
 #> # ℹ 95 more rows
 #> # ℹ 1 more variable: ess_tail <dbl>
 
@@ -211,7 +211,7 @@ 
Examples

 #>  2 alpha         0.364
 #>  3 beta[1]      -0.632
 #>  4 beta[2]      -0.259
-#>  5 beta[3]       0.649
+#>  5 beta[3]       0.648
 #>  6 log_lik[1]   -0.515
 #>  7 log_lik[2]   -0.394
 #>  8 log_lik[3]   -0.469
@@ -222,18 +222,18 @@ 
Examples

 fit_logistic_vb <- cmdstanr_example("logistic", method = "variational")
 fit_logistic_vb$summary()
 #> # A tibble: 106 × 7
-#>    variable       mean  median     sd    mad      q5     q95
-#>    <chr>         <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
-#>  1 lp__        -66.2   -65.8   1.56   1.32   -69.2   -64.3  
-#>  2 lp_approx__  -1.94   -1.61  1.34   1.15    -4.56   -0.362
-#>  3 alpha         0.448   0.454 0.184  0.186    0.144   0.745
-#>  4 beta[1]      -0.710  -0.711 0.297  0.296   -1.20   -0.230
-#>  5 beta[2]      -0.211  -0.214 0.213  0.218   -0.546   0.131
-#>  6 beta[3]       0.719   0.717 0.272  0.278    0.285   1.18 
-#>  7 log_lik[1]   -0.478  -0.474 0.0839 0.0865  -0.622  -0.351
-#>  8 log_lik[2]   -0.391  -0.364 0.154  0.141   -0.698  -0.180
-#>  9 log_lik[3]   -0.408  -0.382 0.181  0.171   -0.755  -0.162
-#> 10 log_lik[4]   -0.483  -0.471 0.145  0.142   -0.745  -0.272
+#>    variable       mean  median    sd   mad      q5      q95
+#>    <chr>         <dbl>   <dbl> <dbl> <dbl>   <dbl>    <dbl>
+#>  1 lp__        -66.4   -65.9   1.84  1.52  -69.9   -64.3   
+#>  2 lp_approx__  -1.98   -1.65  1.42  1.25   -4.64   -0.331 
+#>  3 alpha         0.377   0.370 0.296 0.307  -0.116   0.869 
+#>  4 beta[1]      -0.646  -0.648 0.241 0.234  -1.04   -0.241 
+#>  5 beta[2]      -0.252  -0.257 0.201 0.191  -0.579   0.0845
+#>  6 beta[3]       0.702   0.695 0.280 0.269   0.236   1.16  
+#>  7 log_lik[1]   -0.523  -0.518 0.128 0.130  -0.747  -0.331 
+#>  8 log_lik[2]   -0.398  -0.369 0.168 0.155  -0.716  -0.174 
+#>  9 log_lik[3]   -0.480  -0.450 0.204 0.193  -0.859  -0.211 
+#> 10 log_lik[4]   -0.455  -0.431 0.159 0.160  -0.739  -0.235 
 #> # ℹ 96 more rows
 
 print_example_program("schools")
@@ -254,25 +254,25 @@ 
Examples

 #>   target += normal_lpdf(y | theta, sigma);
 #> }
 fit_schools_mcmc <- cmdstanr_example("schools")
-#> Warning: 679 of 4000 (17.0%) transitions ended with a divergence.
+#> Warning: 260 of 4000 (6.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
-#> Warning: 2 of 4 chains had an E-BFMI less than 0.3.
+#> Warning: 1 of 4 chains had an E-BFMI less than 0.3.
 #> See https://mc-stan.org/misc/warnings for details.
 fit_schools_mcmc$summary()
 #> # A tibble: 11 × 10
-#>    variable   mean median    sd   mad       q5   q95  rhat ess_bulk ess_tail
-#>    <chr>     <dbl>  <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>    <dbl>    <dbl>
-#>  1 lp__     -55.2  -56.2   7.20  8.44 -65.7    -44.5  1.35     9.26   502.  
-#>  2 mu         5.92   4.83  3.74  2.79   0.0739  12.5  1.14   269.     979.  
-#>  3 tau        4.29   3.34  3.72  3.44   0.650   11.7  1.35     9.24     5.53
-#>  4 theta[1]   8.28   6.13  6.22  3.70   0.148   20.3  1.19   199.    1448.  
-#>  5 theta[2]   6.41   5.49  5.11  3.45  -1.72    15.4  1.10   460.    1260.  
-#>  6 theta[3]   5.14   4.33  5.96  3.94  -5.01    15.0  1.23   782.    1447.  
-#>  7 theta[4]   5.87   4.59  5.16  3.49  -2.48    15.2  1.31   730.     948.  
-#>  8 theta[5]   4.56   4.37  5.04  4.04  -4.72    12.5  1.28   643.    1270.  
-#>  9 theta[6]   5.08   4.56  5.20  3.35  -3.77    13.6  1.12   659.    1302.  
-#> 10 theta[7]   8.06   6.46  5.45  3.53   0.928   18.2  1.05    73.8   1255.  
-#> 11 theta[8]   6.38   5.50  6.15  3.54  -2.77    17.5  1.11   260.    1699.  
+#>    variable   mean median    sd   mad      q5   q95  rhat ess_bulk ess_tail
+#>    <chr>     <dbl>  <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl>
+#>  1 lp__     -58.0  -58.4   5.27  5.30 -66.3   -48.5  1.07     43.0     27.8
+#>  2 mu         6.38   6.13  4.15  3.80  -0.237  13.5  1.01    627.     964. 
+#>  3 tau        5.31   4.49  3.57  3.28   1.10   12.2  1.07     37.6     21.2
+#>  4 theta[1]   9.14   8.16  7.00  6.00  -0.685  21.7  1.01    966.    1427. 
+#>  5 theta[2]   6.73   6.51  5.64  4.88  -2.30   16.4  1.02   1127.    1732. 
+#>  6 theta[3]   5.08   5.36  6.58  5.64  -6.24   15.5  1.02    792.    1549. 
+#>  7 theta[4]   6.57   6.30  5.92  5.19  -2.79   16.1  1.01   1328.    1869. 
+#>  8 theta[5]   4.56   4.87  5.47  5.08  -5.00   13.1  1.02    707.    1473. 
+#>  9 theta[6]   5.31   5.53  6.01  5.11  -5.18   14.7  1.01    834.    1685. 
+#> 10 theta[7]   9.04   8.41  5.90  5.30   0.408  19.3  1.01    701.    1390. 
+#> 11 theta[8]   6.88   6.54  6.69  5.37  -4.06   18.2  1.02   1238.    2106. 
 
 print_example_program("schools_ncp")
 #> data {
@@ -297,36 +297,35 @@ 
Examples

 fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")
 fit_schools_ncp_mcmc$summary()
 #> # A tibble: 19 × 10
-#>    variable         mean   median    sd   mad       q5    q95  rhat ess_bulk
-#>    <chr>           <dbl>    <dbl> <dbl> <dbl>    <dbl>  <dbl> <dbl>    <dbl>
-#>  1 lp__         -46.9    -46.6    2.41  2.29  -51.3    -43.5   1.00    1454.
-#>  2 mu             6.51     6.48   4.11  4.18   -0.0368  13.3   1.00    3392.
-#>  3 tau            4.77     3.96   3.63  3.48    0.449   11.8   1.00    2260.
-#>  4 theta_raw[1]   0.377    0.379  0.975 0.954  -1.26     1.95  1.00    4065.
-#>  5 theta_raw[2]   0.0526   0.0555 0.895 0.884  -1.43     1.53  1.00    3436.
-#>  6 theta_raw[3]  -0.143   -0.156  0.970 0.957  -1.73     1.48  1.00    3686.
-#>  7 theta_raw[4]   0.0219   0.0261 0.906 0.930  -1.48     1.50  1.00    3671.
-#>  8 theta_raw[5]  -0.263   -0.285  0.920 0.904  -1.76     1.29  1.00    4086.
-#>  9 theta_raw[6]  -0.130   -0.144  0.930 0.917  -1.64     1.42  1.00    4353.
-#> 10 theta_raw[7]   0.369    0.376  0.918 0.911  -1.15     1.87  1.00    3860.
-#> 11 theta_raw[8]   0.0659   0.0592 0.971 0.988  -1.51     1.69  1.00    3781.
-#> 12 theta[1]       9.01     8.30   6.74  5.76   -0.246   21.2   1.00    3647.
-#> 13 theta[2]       6.88     6.83   5.35  4.89   -1.89    15.7   1.00    4377.
-#> 14 theta[3]       5.45     5.79   6.51  5.55   -5.90    15.5   1.00    3662.
-#> 15 theta[4]       6.73     6.60   5.73  5.22   -2.58    16.1   1.00    3999.
-#> 16 theta[5]       4.91     5.16   5.52  4.90   -4.78    13.6   1.00    4517.
-#> 17 theta[6]       5.64     5.91   6.01  5.33   -4.77    15.0   1.00    4197.
-#> 18 theta[7]       8.87     8.36   5.95  5.37    0.312   19.6   1.00    3846.
-#> 19 theta[8]       7.06     6.77   6.80  5.70   -3.70    18.3   1.00    3866.
-#> # ℹ 1 more variable: ess_tail <dbl>
+#>    variable     mean   median    sd   mad      q5    q95  rhat ess_bulk ess_tail
+#>    <chr>       <dbl>    <dbl> <dbl> <dbl>   <dbl>  <dbl> <dbl>    <dbl>    <dbl>
+#>  1 lp__     -46.9    -4.66e+1 2.40  2.30  -51.2   -43.5   1.00    1569.    2358.
+#>  2 mu         6.51    6.55e+0 4.22  4.14   -0.355  13.3   1.00    3009.    2465.
+#>  3 tau        4.86    4.06e+0 3.67  3.43    0.444  12.0   1.00    1792.    1620.
+#>  4 theta_r…   0.348   3.50e-1 0.956 0.960  -1.26    1.93  1.00    3527.    2677.
+#>  5 theta_r…   0.0410  5.49e-2 0.889 0.880  -1.47    1.46  1.00    4110.    2991.
+#>  6 theta_r…  -0.142  -1.47e-1 0.953 0.945  -1.69    1.43  1.00    4686.    2441.
+#>  7 theta_r…  -0.0107 -9.40e-3 0.937 0.935  -1.53    1.53  1.00    4262.    2854.
+#>  8 theta_r…  -0.296  -2.93e-1 0.931 0.915  -1.83    1.24  1.00    3286.    2516.
+#>  9 theta_r…  -0.172  -1.84e-1 0.911 0.881  -1.69    1.36  1.00    3801.    2532.
+#> 10 theta_r…   0.360   3.84e-1 0.933 0.904  -1.21    1.83  1.00    3900.    2825.
+#> 11 theta_r…   0.0773  7.71e-2 0.982 1.00   -1.54    1.67  1.00    4239.    2766.
+#> 12 theta[1]   9.01    8.06e+0 6.94  5.65   -0.546  22.1   1.00    3731.    3102.
+#> 13 theta[2]   6.85    6.80e+0 5.61  5.13   -2.12   16.3   1.00    4538.    3046.
+#> 14 theta[3]   5.47    5.82e+0 6.48  5.46   -6.06   15.3   1.00    4254.    3124.
+#> 15 theta[4]   6.52    6.47e+0 5.74  5.18   -2.72   15.9   1.00    4625.    3465.
+#> 16 theta[5]   4.79    5.10e+0 5.61  5.15   -4.87   13.3   1.00    4852.    3150.
+#> 17 theta[6]   5.54    5.73e+0 5.60  5.11   -3.94   14.1   1.00    4021.    3062.
+#> 18 theta[7]   8.95    8.27e+0 6.02  5.41    0.317  19.7   1.00    4087.    3495.
+#> 19 theta[8]   7.00    7.00e+0 6.58  5.65   -3.39   17.8   1.00    3992.    3246.
 
 # optimization fails for hierarchical model
 cmdstanr_example("schools", "optimize", quiet = FALSE)
-#> Initial log joint probability = -53.966 
+#> Initial log joint probability = -57.1999 
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>       99       123.351      0.058381   2.09576e+09      0.5049      0.5049      181    
+#>       99       137.364      0.389882   2.12196e+10      0.1758      0.3216      199    
 #>     Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
-#>      184       248.826        0.4674   3.92933e+16       1e-12       0.001      384  LS failed, Hessian reset  
+#>      175       252.319     0.0285374   7.72538e+16       1e-12       0.001      386  LS failed, Hessian reset  
 #> Chain 1 Optimization terminated with error: 
 #> Chain 1   Line search failed to achieve a sufficient decrease, no more progress can be made
 #> Warning: Fitting finished unexpectedly! Use the $output() method for more information.
diff --git a/docs/reference/cmdstanr_global_options.html b/docs/reference/cmdstanr_global_options.html
index 879820a9f..97ca7fb7f 100644
--- a/docs/reference/cmdstanr_global_options.html
+++ b/docs/reference/cmdstanr_global_options.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     

 
@@ -120,6 +120,8 @@ 
Details

 compile for more details. The default is FALSE.

 
  • cmdstanr_max_rows: The maximum number of rows of output to print when
 using the $print() method. The default is 10.
    • 
+
  • cmdstanr_print_line_numbers: Should line numbers be included when
+printing a Stan program? The default is FALSE.
    • 
 
  • cmdstanr_no_ver_check: Should the check for a more recent version of
 CmdStan be disabled? The default is FALSE.
    • 
 
  • cmdstanr_output_dir: The directory where CmdStan should write its output
diff --git a/docs/reference/draws_to_csv.html b/docs/reference/draws_to_csv.html
index 2ce97f972..a87098b92 100644
--- a/docs/reference/draws_to_csv.html
+++ b/docs/reference/draws_to_csv.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    • 
 
@@ -166,10 +166,10 @@ 
    Examples
    
 
 draws_csv_files <- draws_to_csv(draws)
 print(draws_csv_files)
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpiACQ3q/fittedParams-202407021533-1-2ac97c.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpiACQ3q/fittedParams-202407021533-2-2ac97c.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpiACQ3q/fittedParams-202407021533-3-2ac97c.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpiACQ3q/fittedParams-202407021533-4-2ac97c.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpWzIPg0/fittedParams-202503310842-1-4146ef.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpWzIPg0/fittedParams-202503310842-2-4146ef.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpWzIPg0/fittedParams-202503310842-3-4146ef.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T//RtmpWzIPg0/fittedParams-202503310842-4-4146ef.csv"
 
 # draws_csv_files <- draws_to_csv(draws,
 #                                 sampler_diagnostic = sampler_diagnostics,
diff --git a/docs/reference/eng_cmdstan.html b/docs/reference/eng_cmdstan.html
index 6deca41f0..a00dec670 100644
--- a/docs/reference/eng_cmdstan.html
+++ b/docs/reference/eng_cmdstan.html
@@ -22,7 +22,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-cmdstan_summary.html b/docs/reference/fit-method-cmdstan_summary.html
index 6bbc27276..32f0e4859 100644
--- a/docs/reference/fit-method-cmdstan_summary.html
+++ b/docs/reference/fit-method-cmdstan_summary.html
@@ -28,7 +28,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -149,8 +149,6 @@ 
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example("logistic")
 fit$cmdstan_diagnose()
-#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021533-1-42dde2.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021533-2-42dde2.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021533-3-42dde2.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021533-4-42dde2.csv
-#> 
 #> Checking sampler transitions treedepth.
 #> Treedepth satisfactory for all transitions.
 #> 
@@ -160,137 +158,136 @@ 
    Examples
    
 #> Checking E-BFMI - sampler transitions HMC potential energy.
 #> E-BFMI satisfactory.
 #> 
-#> Effective sample size satisfactory.
+#> Rank-normalized split effective sample size satisfactory for all parameters.
 #> 
-#> Split R-hat values satisfactory all parameters.
+#> Rank-normalized split R-hat values satisfactory for all parameters.
 #> 
 #> Processing complete, no problems detected.
 fit$cmdstan_summary()
 #> Inference for Stan model: logistic_model
-#> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
+#> 4 chains: each with iter=1000; warmup=1000; thin=1; 1000 iterations saved.
 #> 
-#> Warmup took (0.043, 0.046, 0.042, 0.041) seconds, 0.17 seconds total
-#> Sampling took (0.15, 0.15, 0.16, 0.15) seconds, 0.61 seconds total
+#> Warmup took (0.023, 0.023, 0.022, 0.021) seconds, 0.089 seconds total
+#> Sampling took (0.078, 0.073, 0.074, 0.072) seconds, 0.30 seconds total
 #> 
-#>                     Mean     MCSE  StdDev     5%       50%       95%  N_Eff  N_Eff/s    R_hat
+#>                     Mean     MCSE  StdDev    MAD     5%       50%       95%  ESS_bulk  ESS_tail  R_hat
 #> 
-#> lp__            -6.6e+01  3.3e-02     1.4    -69  -6.6e+01  -6.4e+01   1837     3007      1.0
-#> accept_stat__       0.90    0.011    0.12   0.65      0.94       1.0    123      202  1.0e+00
-#> stepsize__          0.77    0.038   0.054   0.73      0.77      0.86    2.0      3.3  1.9e+13
-#> treedepth__          2.3     0.10    0.51    2.0       2.0       3.0     26       42  1.1e+00
-#> n_leapfrog__         4.9     0.35     2.0    3.0       3.0       7.0     34       55  1.0e+00
-#> divergent__         0.00      nan    0.00   0.00      0.00      0.00    nan      nan      nan
-#> energy__              68    0.049     2.0     65        68        72   1652     2704  1.0e+00
+#> lp__            -6.6e+01  3.1e-02     1.4    1.2    -69  -6.6e+01  -6.4e+01      2077      2863    1.0
+#> accept_stat__       0.91  1.4e-03    0.10  0.072   0.71      0.95       1.0      5781      4282    1.0
+#> stepsize__          0.72      nan   0.040  0.039   0.66      0.73      0.77       nan       nan    nan
+#> treedepth__          2.4  1.5e-02    0.53   0.00    2.0       2.0       3.0      1630      1360    1.0
+#> n_leapfrog__         5.3  9.5e-02     2.0   0.00    3.0       7.0       7.0      3307       884    1.0
+#> divergent__         0.00      nan    0.00   0.00   0.00      0.00      0.00       nan       nan    nan
+#> energy__              68  4.7e-02     2.0    1.8     65        68        72      1774      2711    1.0
 #> 
-#> alpha            3.8e-01  3.5e-03    0.22  0.023   3.8e-01   7.4e-01   3919     6414      1.0
-#> beta[1]         -6.7e-01  3.8e-03    0.25   -1.1  -6.7e-01  -2.6e-01   4387     7180      1.0
-#> beta[2]         -2.7e-01  3.5e-03    0.23  -0.64  -2.7e-01   1.1e-01   4209     6889     1.00
-#> beta[3]          6.8e-01  4.3e-03    0.26   0.25   6.7e-01   1.1e+00   3682     6026     1.00
-#> log_lik[1]      -5.1e-01  1.5e-03   0.099  -0.69  -5.1e-01  -3.6e-01   4109     6725      1.0
-#> log_lik[2]      -4.0e-01  2.2e-03    0.15  -0.67  -3.8e-01  -2.0e-01   4736     7751      1.0
-#> log_lik[3]      -5.0e-01  3.4e-03    0.22  -0.90  -4.6e-01  -2.0e-01   4043     6617      1.0
-#> log_lik[4]      -4.5e-01  2.4e-03    0.15  -0.73  -4.3e-01  -2.4e-01   3941     6450      1.0
-#> log_lik[5]      -1.2e+00  4.5e-03    0.28   -1.7  -1.2e+00  -7.7e-01   3844     6291     1.00
-#> log_lik[6]      -5.9e-01  2.9e-03    0.19  -0.93  -5.7e-01  -3.2e-01   4123     6748     1.00
-#> log_lik[7]      -6.4e-01  1.9e-03    0.13  -0.87  -6.3e-01  -4.5e-01   4625     7569      1.0
-#> log_lik[8]      -2.8e-01  2.1e-03    0.13  -0.53  -2.5e-01  -1.1e-01   3984     6520     1.00
-#> log_lik[9]      -6.9e-01  2.6e-03    0.17  -0.99  -6.8e-01  -4.4e-01   4294     7028      1.0
-#> log_lik[10]     -7.4e-01  3.7e-03    0.23   -1.2  -7.1e-01  -4.0e-01   3964     6488      1.0
-#> log_lik[11]     -2.8e-01  2.1e-03    0.12  -0.51  -2.6e-01  -1.2e-01   3501     5729     1.00
-#> log_lik[12]     -5.0e-01  3.5e-03    0.23  -0.93  -4.6e-01  -1.9e-01   4348     7116      1.0
-#> log_lik[13]     -6.6e-01  3.2e-03    0.21   -1.0  -6.3e-01  -3.5e-01   4224     6914     1.00
-#> log_lik[14]     -3.6e-01  2.7e-03    0.17  -0.68  -3.3e-01  -1.3e-01   4013     6568      1.0
-#> log_lik[15]     -2.8e-01  1.8e-03    0.11  -0.47  -2.6e-01  -1.3e-01   3538     5790     1.00
-#> log_lik[16]     -2.8e-01  1.4e-03   0.086  -0.43  -2.7e-01  -1.5e-01   3558     5822     1.00
-#> log_lik[17]     -1.6e+00  4.8e-03    0.29   -2.1  -1.6e+00  -1.2e+00   3654     5981      1.0
-#> log_lik[18]     -4.8e-01  1.6e-03    0.11  -0.67  -4.7e-01  -3.1e-01   4457     7294     1.00
-#> log_lik[19]     -2.3e-01  1.2e-03   0.075  -0.37  -2.3e-01  -1.2e-01   3731     6107     1.00
-#> log_lik[20]     -1.1e-01  1.4e-03   0.080  -0.26  -9.3e-02  -2.7e-02   3471     5681      1.0
-#> log_lik[21]     -2.1e-01  1.5e-03   0.086  -0.37  -2.0e-01  -9.3e-02   3393     5554     1.00
-#> log_lik[22]     -5.7e-01  2.3e-03    0.15  -0.83  -5.6e-01  -3.5e-01   4157     6803     1.00
-#> log_lik[23]     -3.3e-01  2.1e-03    0.14  -0.60  -3.1e-01  -1.4e-01   4351     7120     1.00
-#> log_lik[24]     -1.4e-01  1.1e-03   0.066  -0.26  -1.2e-01  -5.2e-02   3635     5949     1.00
-#> log_lik[25]     -4.6e-01  1.8e-03    0.12  -0.68  -4.5e-01  -2.8e-01   4638     7591      1.0
-#> log_lik[26]     -1.5e+00  5.1e-03    0.34   -2.1  -1.5e+00  -1.0e+00   4383     7173     1.00
-#> log_lik[27]     -3.1e-01  2.0e-03    0.12  -0.52  -2.9e-01  -1.4e-01   3553     5815     1.00
-#> log_lik[28]     -4.4e-01  1.3e-03   0.083  -0.59  -4.4e-01  -3.2e-01   4086     6688      1.0
-#> log_lik[29]     -7.3e-01  3.4e-03    0.23   -1.1  -7.0e-01  -3.9e-01   4476     7326     1.00
-#> log_lik[30]     -6.9e-01  2.9e-03    0.19   -1.0  -6.8e-01  -4.1e-01   4511     7383      1.0
-#> log_lik[31]     -4.9e-01  2.6e-03    0.16  -0.78  -4.7e-01  -2.6e-01   3899     6381     1.00
-#> log_lik[32]     -4.2e-01  1.8e-03    0.11  -0.61  -4.1e-01  -2.7e-01   3671     6009     1.00
-#> log_lik[33]     -4.1e-01  1.9e-03    0.13  -0.65  -4.0e-01  -2.3e-01   4622     7564      1.0
-#> log_lik[34]     -6.4e-02  8.5e-04   0.050  -0.16  -5.0e-02  -1.3e-02   3474     5686     1.00
-#> log_lik[35]     -5.9e-01  2.6e-03    0.18  -0.92  -5.7e-01  -3.2e-01   4937     8080     1.00
-#> log_lik[36]     -3.2e-01  1.9e-03    0.13  -0.57  -3.1e-01  -1.5e-01   4737     7753     1.00
-#> log_lik[37]     -7.0e-01  3.5e-03    0.23   -1.1  -6.7e-01  -3.7e-01   4158     6805      1.0
-#> log_lik[38]     -3.1e-01  2.4e-03    0.15  -0.60  -2.9e-01  -1.2e-01   4035     6604     1.00
-#> log_lik[39]     -1.8e-01  1.8e-03    0.11  -0.39  -1.5e-01  -5.2e-02   3644     5964      1.0
-#> log_lik[40]     -6.8e-01  1.9e-03    0.13  -0.91  -6.7e-01  -4.8e-01   4470     7316      1.0
-#> log_lik[41]     -1.1e+00  4.1e-03    0.25   -1.6  -1.1e+00  -7.6e-01   3765     6162     1.00
-#> log_lik[42]     -9.3e-01  3.0e-03    0.20   -1.3  -9.1e-01  -6.3e-01   4354     7127      1.0
-#> log_lik[43]     -4.1e-01  3.7e-03    0.26  -0.91  -3.5e-01  -1.0e-01   4788     7836     1.00
-#> log_lik[44]     -1.2e+00  3.1e-03    0.19   -1.5  -1.2e+00  -8.9e-01   3623     5929      1.0
-#> log_lik[45]     -3.6e-01  1.8e-03    0.12  -0.57  -3.4e-01  -1.9e-01   4021     6581      1.0
-#> log_lik[46]     -5.8e-01  1.9e-03    0.13  -0.81  -5.7e-01  -3.9e-01   4786     7833      1.0
-#> log_lik[47]     -3.1e-01  2.0e-03    0.13  -0.55  -2.9e-01  -1.4e-01   4246     6949      1.0
-#> log_lik[48]     -3.2e-01  1.3e-03   0.082  -0.47  -3.2e-01  -2.0e-01   4166     6819     1.00
-#> log_lik[49]     -3.2e-01  1.3e-03   0.079  -0.46  -3.1e-01  -2.0e-01   3546     5804     1.00
-#> log_lik[50]     -1.3e+00  4.9e-03    0.33   -1.9  -1.3e+00  -8.0e-01   4492     7352     1.00
-#> log_lik[51]     -2.9e-01  1.4e-03   0.093  -0.46  -2.8e-01  -1.6e-01   4451     7284     1.00
-#> log_lik[52]     -8.4e-01  2.2e-03    0.14   -1.1  -8.3e-01  -6.2e-01   4245     6948      1.0
-#> log_lik[53]     -4.0e-01  2.1e-03    0.13  -0.63  -3.9e-01  -2.2e-01   3748     6134     1.00
-#> log_lik[54]     -3.7e-01  2.2e-03    0.14  -0.63  -3.5e-01  -1.7e-01   4130     6759      1.0
-#> log_lik[55]     -3.9e-01  2.0e-03    0.14  -0.64  -3.7e-01  -2.0e-01   4649     7608      1.0
-#> log_lik[56]     -3.2e-01  2.8e-03    0.19  -0.69  -2.8e-01  -9.5e-02   4610     7546     1.00
-#> log_lik[57]     -6.6e-01  1.8e-03    0.12  -0.86  -6.5e-01  -4.7e-01   4203     6879      1.0
-#> log_lik[58]     -9.5e-01  5.3e-03    0.36   -1.6  -9.0e-01  -4.4e-01   4498     7362     1.00
-#> log_lik[59]     -1.4e+00  5.4e-03    0.34   -2.0  -1.3e+00  -8.4e-01   4097     6706      1.0
-#> log_lik[60]     -9.8e-01  2.5e-03    0.16   -1.3  -9.7e-01  -7.3e-01   4050     6628      1.0
-#> log_lik[61]     -5.4e-01  1.5e-03   0.098  -0.71  -5.3e-01  -3.9e-01   4382     7171      1.0
-#> log_lik[62]     -8.8e-01  4.7e-03    0.31   -1.4  -8.4e-01  -4.4e-01   4166     6818     1.00
-#> log_lik[63]     -1.2e-01  1.2e-03   0.072  -0.25  -1.0e-01  -3.2e-02   3346     5477     1.00
-#> log_lik[64]     -9.0e-01  3.7e-03    0.25   -1.4  -8.8e-01  -5.3e-01   4389     7183     1.00
-#> log_lik[65]     -2.0e+00  9.9e-03    0.58   -3.0  -2.0e+00  -1.1e+00   3440     5629     1.00
-#> log_lik[66]     -5.1e-01  2.2e-03    0.14  -0.75  -5.0e-01  -3.1e-01   3804     6226     1.00
-#> log_lik[67]     -2.8e-01  1.3e-03   0.081  -0.42  -2.7e-01  -1.6e-01   4007     6558     1.00
-#> log_lik[68]     -1.1e+00  3.7e-03    0.24   -1.5  -1.0e+00  -7.0e-01   4155     6800     1.00
-#> log_lik[69]     -4.3e-01  1.4e-03   0.084  -0.58  -4.3e-01  -3.1e-01   3753     6142     1.00
-#> log_lik[70]     -6.4e-01  3.5e-03    0.24   -1.1  -6.1e-01  -3.1e-01   4500     7365     1.00
-#> log_lik[71]     -6.1e-01  3.0e-03    0.21  -0.99  -5.8e-01  -3.1e-01   4571     7481     1.00
-#> log_lik[72]     -4.6e-01  2.6e-03    0.17  -0.78  -4.4e-01  -2.2e-01   4261     6974     1.00
-#> log_lik[73]     -1.5e+00  5.8e-03    0.37   -2.1  -1.5e+00  -9.2e-01   4023     6585      1.0
-#> log_lik[74]     -9.5e-01  3.1e-03    0.20   -1.3  -9.4e-01  -6.5e-01   4106     6721     1.00
-#> log_lik[75]     -1.1e+00  5.9e-03    0.38   -1.9  -1.1e+00  -5.9e-01   4198     6870     1.00
-#> log_lik[76]     -3.7e-01  2.2e-03    0.14  -0.63  -3.5e-01  -1.8e-01   3982     6517      1.0
-#> log_lik[77]     -8.8e-01  2.2e-03    0.14   -1.1  -8.7e-01  -6.6e-01   4043     6618      1.0
-#> log_lik[78]     -4.8e-01  2.5e-03    0.17  -0.79  -4.6e-01  -2.5e-01   4533     7418     1.00
-#> log_lik[79]     -7.6e-01  3.0e-03    0.19   -1.1  -7.5e-01  -4.8e-01   4054     6635      1.0
-#> log_lik[80]     -5.4e-01  2.8e-03    0.19  -0.89  -5.2e-01  -2.7e-01   4782     7827      1.0
-#> log_lik[81]     -1.6e-01  1.6e-03    0.10  -0.37  -1.4e-01  -4.9e-02   4161     6810      1.0
-#> log_lik[82]     -2.2e-01  2.1e-03    0.14  -0.49  -1.9e-01  -6.5e-02   4340     7103      1.0
-#> log_lik[83]     -3.4e-01  1.3e-03   0.081  -0.48  -3.4e-01  -2.2e-01   3663     5996      1.0
-#> log_lik[84]     -2.7e-01  1.5e-03   0.092  -0.44  -2.6e-01  -1.5e-01   3524     5767     1.00
-#> log_lik[85]     -1.3e-01  1.3e-03   0.076  -0.28  -1.1e-01  -4.1e-02   3532     5780      1.0
-#> log_lik[86]     -1.1e+00  4.8e-03    0.32   -1.7  -1.1e+00  -6.6e-01   4283     7011      1.0
-#> log_lik[87]     -8.2e-01  1.9e-03    0.13   -1.0  -8.2e-01  -6.3e-01   4347     7115      1.0
-#> log_lik[88]     -7.8e-01  3.7e-03    0.24   -1.2  -7.5e-01  -4.3e-01   4170     6825     1.00
-#> log_lik[89]     -1.3e+00  5.0e-03    0.32   -1.8  -1.2e+00  -7.9e-01   4012     6566      1.0
-#> log_lik[90]     -2.6e-01  2.1e-03    0.14  -0.53  -2.4e-01  -9.3e-02   4313     7058     1.00
-#> log_lik[91]     -3.9e-01  1.9e-03    0.13  -0.63  -3.7e-01  -2.1e-01   4620     7562      1.0
-#> log_lik[92]     -1.5e+00  5.7e-03    0.34   -2.1  -1.5e+00  -9.8e-01   3518     5758     1.00
-#> log_lik[93]     -7.4e-01  3.5e-03    0.22   -1.1  -7.2e-01  -4.2e-01   3871     6336     1.00
-#> log_lik[94]     -3.2e-01  1.4e-03   0.088  -0.48  -3.1e-01  -1.9e-01   3732     6107      1.0
-#> log_lik[95]     -3.9e-01  1.8e-03    0.11  -0.58  -3.8e-01  -2.3e-01   3830     6269     1.00
-#> log_lik[96]     -1.6e+00  4.8e-03    0.28   -2.1  -1.6e+00  -1.1e+00   3457     5657      1.0
-#> log_lik[97]     -4.3e-01  1.5e-03    0.10  -0.61  -4.2e-01  -2.8e-01   4740     7758     1.00
-#> log_lik[98]     -1.0e+00  5.5e-03    0.37   -1.7  -1.0e+00  -5.1e-01   4582     7499     1.00
-#> log_lik[99]     -6.9e-01  2.1e-03    0.14  -0.95  -6.8e-01  -4.8e-01   4565     7472      1.0
-#> log_lik[100]    -3.9e-01  1.5e-03   0.096  -0.56  -3.8e-01  -2.5e-01   4243     6945      1.0
+#> alpha            3.7e-01  3.4e-03    0.22   0.22  0.028   3.7e-01   7.3e-01      4093      2683    1.0
+#> beta[1]         -6.6e-01  3.9e-03    0.25   0.25   -1.1  -6.6e-01  -2.6e-01      4257      3329    1.0
+#> beta[2]         -2.7e-01  3.6e-03    0.22   0.23  -0.64  -2.6e-01   9.8e-02      3945      3224    1.0
+#> beta[3]          6.7e-01  4.3e-03    0.27   0.27   0.25   6.6e-01   1.1e+00      3898      2767   1.00
+#> log_lik[1]      -5.2e-01  1.5e-03   0.098  0.098  -0.69  -5.1e-01  -3.7e-01      4210      2902    1.0
+#> log_lik[2]      -4.0e-01  2.2e-03    0.14   0.14  -0.67  -3.9e-01  -2.0e-01      4291      2935    1.0
+#> log_lik[3]      -5.0e-01  3.3e-03    0.21   0.20  -0.90  -4.6e-01  -2.1e-01      4240      2834    1.0
+#> log_lik[4]      -4.5e-01  2.5e-03    0.16   0.15  -0.73  -4.3e-01  -2.4e-01      3795      3034    1.0
+#> log_lik[5]      -1.2e+00  4.4e-03    0.28   0.28   -1.7  -1.2e+00  -7.5e-01      4168      2944    1.0
+#> log_lik[6]      -5.9e-01  3.0e-03    0.19   0.19  -0.93  -5.8e-01  -3.3e-01      3850      2768   1.00
+#> log_lik[7]      -6.4e-01  1.9e-03    0.13   0.12  -0.87  -6.3e-01  -4.5e-01      4232      3044    1.0
+#> log_lik[8]      -2.8e-01  2.2e-03    0.13   0.12  -0.53  -2.6e-01  -1.1e-01      3612      3023    1.0
+#> log_lik[9]      -6.9e-01  2.6e-03    0.16   0.16  -0.98  -6.8e-01  -4.5e-01      4078      3164    1.0
+#> log_lik[10]     -7.4e-01  3.7e-03    0.23   0.23   -1.2  -7.1e-01  -4.0e-01      4026      2967    1.0
+#> log_lik[11]     -2.8e-01  2.1e-03    0.13   0.12  -0.52  -2.6e-01  -1.2e-01      3419      2597    1.0
+#> log_lik[12]     -5.0e-01  3.6e-03    0.24   0.22  -0.94  -4.7e-01  -1.9e-01      4359      3260    1.0
+#> log_lik[13]     -6.5e-01  3.3e-03    0.21   0.21   -1.0  -6.3e-01  -3.6e-01      4071      2663    1.0
+#> log_lik[14]     -3.6e-01  2.7e-03    0.17   0.16  -0.68  -3.3e-01  -1.4e-01      4196      2966    1.0
+#> log_lik[15]     -2.8e-01  1.7e-03    0.11   0.10  -0.47  -2.6e-01  -1.4e-01      4062      2457    1.0
+#> log_lik[16]     -2.8e-01  1.5e-03   0.087  0.085  -0.44  -2.7e-01  -1.5e-01      3304      2815    1.0
+#> log_lik[17]     -1.6e+00  4.8e-03    0.29   0.29   -2.1  -1.6e+00  -1.1e+00      3624      2964    1.0
+#> log_lik[18]     -4.8e-01  1.7e-03    0.10   0.10  -0.66  -4.8e-01  -3.2e-01      3932      2824    1.0
+#> log_lik[19]     -2.4e-01  1.3e-03   0.075  0.074  -0.37  -2.3e-01  -1.3e-01      3554      3085    1.0
+#> log_lik[20]     -1.1e-01  1.3e-03   0.079  0.061  -0.27  -9.5e-02  -3.0e-02      4170      3008    1.0
+#> log_lik[21]     -2.2e-01  1.5e-03   0.089  0.084  -0.38  -2.0e-01  -9.7e-02      3302      2740    1.0
+#> log_lik[22]     -5.7e-01  2.4e-03    0.14   0.14  -0.83  -5.6e-01  -3.6e-01      3779      3160    1.0
+#> log_lik[23]     -3.3e-01  2.2e-03    0.14   0.13  -0.58  -3.1e-01  -1.5e-01      3934      3316    1.0
+#> log_lik[24]     -1.4e-01  1.1e-03   0.067  0.061  -0.27  -1.3e-01  -5.4e-02      3656      3128    1.0
+#> log_lik[25]     -4.6e-01  1.9e-03    0.12   0.12  -0.68  -4.4e-01  -2.8e-01      4029      2992    1.0
+#> log_lik[26]     -1.5e+00  5.2e-03    0.34   0.33   -2.1  -1.5e+00  -9.9e-01      4325      3387    1.0
+#> log_lik[27]     -3.1e-01  2.1e-03    0.12   0.12  -0.54  -2.9e-01  -1.5e-01      3375      2563    1.0
+#> log_lik[28]     -4.5e-01  1.3e-03   0.082  0.082  -0.59  -4.4e-01  -3.2e-01      3776      2975    1.0
+#> log_lik[29]     -7.3e-01  3.3e-03    0.23   0.23   -1.1  -7.0e-01  -3.9e-01      4688      3192    1.0
+#> log_lik[30]     -7.0e-01  2.9e-03    0.18   0.18   -1.0  -6.8e-01  -4.2e-01      4197      3162    1.0
+#> log_lik[31]     -4.9e-01  2.7e-03    0.16   0.16  -0.79  -4.7e-01  -2.6e-01      3604      2908    1.0
+#> log_lik[32]     -4.3e-01  1.7e-03    0.11   0.11  -0.62  -4.2e-01  -2.7e-01      3793      2685    1.0
+#> log_lik[33]     -4.1e-01  2.0e-03    0.13   0.12  -0.65  -3.9e-01  -2.3e-01      4149      2968    1.0
+#> log_lik[34]     -6.6e-02  8.7e-04   0.052  0.038  -0.16  -5.2e-02  -1.3e-02      3571      2878    1.0
+#> log_lik[35]     -5.9e-01  2.8e-03    0.19   0.19  -0.92  -5.6e-01  -3.2e-01      4485      3033    1.0
+#> log_lik[36]     -3.3e-01  2.0e-03    0.13   0.12  -0.56  -3.1e-01  -1.5e-01      4361      3167   1.00
+#> log_lik[37]     -7.0e-01  3.4e-03    0.23   0.22   -1.1  -6.7e-01  -3.7e-01      4430      3268    1.0
+#> log_lik[38]     -3.2e-01  2.5e-03    0.15   0.14  -0.61  -2.9e-01  -1.2e-01      3889      3062    1.0
+#> log_lik[39]     -1.8e-01  1.7e-03    0.11  0.089  -0.38  -1.6e-01  -5.5e-02      4256      2773    1.0
+#> log_lik[40]     -6.8e-01  2.0e-03    0.12   0.12  -0.90  -6.7e-01  -4.9e-01      4119      3108    1.0
+#> log_lik[41]     -1.1e+00  4.3e-03    0.25   0.25   -1.6  -1.1e+00  -7.5e-01      3475      2646    1.0
+#> log_lik[42]     -9.3e-01  3.0e-03    0.19   0.19   -1.3  -9.2e-01  -6.3e-01      4203      3085    1.0
+#> log_lik[43]     -4.1e-01  3.9e-03    0.26   0.22  -0.91  -3.5e-01  -1.1e-01      4971      3223   1.00
+#> log_lik[44]     -1.2e+00  2.9e-03    0.18   0.18   -1.5  -1.2e+00  -8.9e-01      3992      2815    1.0
+#> log_lik[45]     -3.6e-01  1.8e-03    0.12   0.11  -0.57  -3.4e-01  -1.9e-01      4297      3077   1.00
+#> log_lik[46]     -5.8e-01  1.9e-03    0.13   0.12  -0.81  -5.7e-01  -3.9e-01      4228      3113    1.0
+#> log_lik[47]     -3.1e-01  2.1e-03    0.13   0.12  -0.55  -2.9e-01  -1.4e-01      3831      2919    1.0
+#> log_lik[48]     -3.3e-01  1.3e-03   0.082  0.081  -0.47  -3.2e-01  -2.1e-01      3891      3283    1.0
+#> log_lik[49]     -3.2e-01  1.3e-03   0.079  0.078  -0.46  -3.2e-01  -2.0e-01      3475      2682    1.0
+#> log_lik[50]     -1.3e+00  4.9e-03    0.32   0.32   -1.8  -1.3e+00  -7.9e-01      4610      3388    1.0
+#> log_lik[51]     -2.9e-01  1.4e-03   0.093  0.090  -0.46  -2.8e-01  -1.6e-01      4207      3215    1.0
+#> log_lik[52]     -8.3e-01  2.2e-03    0.14   0.14   -1.1  -8.3e-01  -6.1e-01      4205      3039    1.0
+#> log_lik[53]     -4.1e-01  2.2e-03    0.13   0.12  -0.64  -3.9e-01  -2.2e-01      3463      2821    1.0
+#> log_lik[54]     -3.7e-01  2.2e-03    0.14   0.13  -0.63  -3.5e-01  -1.8e-01      4357      3109   1.00
+#> log_lik[55]     -3.9e-01  2.1e-03    0.13   0.13  -0.63  -3.7e-01  -2.0e-01      4184      2870    1.0
+#> log_lik[56]     -3.2e-01  2.8e-03    0.19   0.17  -0.67  -2.8e-01  -9.4e-02      4725      2892    1.0
+#> log_lik[57]     -6.6e-01  1.8e-03    0.12   0.12  -0.86  -6.5e-01  -4.8e-01      4204      3006    1.0
+#> log_lik[58]     -9.5e-01  5.4e-03    0.36   0.35   -1.6  -9.0e-01  -4.5e-01      4724      2916    1.0
+#> log_lik[59]     -1.4e+00  5.4e-03    0.34   0.33   -2.0  -1.3e+00  -8.4e-01      4126      2944    1.0
+#> log_lik[60]     -9.8e-01  2.4e-03    0.16   0.16   -1.3  -9.7e-01  -7.3e-01      4237      3028    1.0
+#> log_lik[61]     -5.4e-01  1.5e-03   0.097  0.096  -0.71  -5.4e-01  -3.9e-01      4260      3064    1.0
+#> log_lik[62]     -8.8e-01  4.8e-03    0.31   0.31   -1.4  -8.5e-01  -4.4e-01      4420      3420    1.0
+#> log_lik[63]     -1.2e-01  1.3e-03   0.075  0.063  -0.26  -1.0e-01  -3.4e-02      3288      2867    1.0
+#> log_lik[64]     -9.0e-01  3.6e-03    0.24   0.23   -1.3  -8.7e-01  -5.5e-01      4286      3272    1.0
+#> log_lik[65]     -2.0e+00  1.0e-02    0.59   0.61   -3.0  -2.0e+00  -1.1e+00      3390      2645    1.0
+#> log_lik[66]     -5.1e-01  2.1e-03    0.14   0.13  -0.75  -5.0e-01  -3.1e-01      4289      2934    1.0
+#> log_lik[67]     -2.8e-01  1.3e-03   0.081  0.080  -0.42  -2.7e-01  -1.6e-01      3650      3029    1.0
+#> log_lik[68]     -1.1e+00  3.7e-03    0.23   0.22   -1.5  -1.0e+00  -7.1e-01      3844      3244    1.0
+#> log_lik[69]     -4.4e-01  1.4e-03   0.083  0.084  -0.58  -4.3e-01  -3.1e-01      3763      2707    1.0
+#> log_lik[70]     -6.4e-01  3.4e-03    0.23   0.21   -1.1  -6.1e-01  -3.2e-01      4692      3113    1.0
+#> log_lik[71]     -6.1e-01  3.3e-03    0.21   0.21  -0.99  -5.8e-01  -3.1e-01      4276      3089    1.0
+#> log_lik[72]     -4.6e-01  2.6e-03    0.17   0.16  -0.78  -4.4e-01  -2.3e-01      4326      3218    1.0
+#> log_lik[73]     -1.5e+00  6.0e-03    0.37   0.37   -2.1  -1.4e+00  -9.1e-01      3893      3087    1.0
+#> log_lik[74]     -9.5e-01  2.9e-03    0.19   0.20   -1.3  -9.3e-01  -6.5e-01      4399      2879    1.0
+#> log_lik[75]     -1.1e+00  5.8e-03    0.38   0.38   -1.8  -1.1e+00  -5.8e-01      4471      3388    1.0
+#> log_lik[76]     -3.7e-01  2.1e-03    0.14   0.13  -0.62  -3.5e-01  -1.8e-01      4223      2860   1.00
+#> log_lik[77]     -8.8e-01  2.2e-03    0.14   0.14   -1.1  -8.7e-01  -6.7e-01      3901      2655    1.0
+#> log_lik[78]     -4.9e-01  2.6e-03    0.17   0.16  -0.80  -4.6e-01  -2.5e-01      4623      3544   1.00
+#> log_lik[79]     -7.6e-01  3.0e-03    0.19   0.19   -1.1  -7.5e-01  -4.8e-01      4156      3125    1.0
+#> log_lik[80]     -5.4e-01  2.8e-03    0.19   0.18  -0.88  -5.2e-01  -2.7e-01      4708      3185    1.0
+#> log_lik[81]     -1.7e-01  1.7e-03    0.10  0.084  -0.37  -1.4e-01  -4.9e-02      4002      3125    1.0
+#> log_lik[82]     -2.2e-01  2.1e-03    0.14   0.11  -0.48  -1.9e-01  -6.6e-02      4234      2703    1.0
+#> log_lik[83]     -3.5e-01  1.3e-03   0.080  0.079  -0.49  -3.4e-01  -2.2e-01      3872      2864   1.00
+#> log_lik[84]     -2.8e-01  1.5e-03   0.091  0.087  -0.44  -2.7e-01  -1.5e-01      4033      2778    1.0
+#> log_lik[85]     -1.3e-01  1.2e-03   0.075  0.063  -0.28  -1.2e-01  -4.3e-02      4008      3071    1.0
+#> log_lik[86]     -1.1e+00  4.8e-03    0.31   0.30   -1.7  -1.1e+00  -6.7e-01      4424      2829    1.0
+#> log_lik[87]     -8.2e-01  1.9e-03    0.13   0.12   -1.0  -8.1e-01  -6.3e-01      4199      3091    1.0
+#> log_lik[88]     -7.7e-01  3.9e-03    0.24   0.24   -1.2  -7.4e-01  -4.3e-01      3979      2725   1.00
+#> log_lik[89]     -1.3e+00  5.0e-03    0.32   0.31   -1.8  -1.2e+00  -8.0e-01      4105      2643    1.0
+#> log_lik[90]     -2.7e-01  2.2e-03    0.14   0.12  -0.54  -2.4e-01  -9.4e-02      4059      3128    1.0
+#> log_lik[91]     -3.9e-01  2.0e-03    0.13   0.12  -0.63  -3.7e-01  -2.0e-01      4231      3276    1.0
+#> log_lik[92]     -1.5e+00  5.5e-03    0.34   0.34   -2.1  -1.5e+00  -9.7e-01      3905      2575    1.0
+#> log_lik[93]     -7.4e-01  3.5e-03    0.22   0.22   -1.1  -7.2e-01  -4.2e-01      3995      2990    1.0
+#> log_lik[94]     -3.2e-01  1.4e-03   0.087  0.084  -0.48  -3.1e-01  -1.9e-01      4054      2800   1.00
+#> log_lik[95]     -3.9e-01  1.9e-03    0.11   0.11  -0.59  -3.8e-01  -2.3e-01      3454      2901    1.0
+#> log_lik[96]     -1.6e+00  4.6e-03    0.28   0.28   -2.1  -1.5e+00  -1.1e+00      3721      3096    1.0
+#> log_lik[97]     -4.3e-01  1.5e-03   0.098  0.095  -0.60  -4.2e-01  -2.8e-01      4401      3224    1.0
+#> log_lik[98]     -1.0e+00  5.7e-03    0.38   0.37   -1.7  -1.0e+00  -5.2e-01      4630      2912    1.0
+#> log_lik[99]     -6.9e-01  2.1e-03    0.14   0.13  -0.94  -6.8e-01  -4.8e-01      4146      3139    1.0
+#> log_lik[100]    -3.9e-01  1.5e-03   0.096  0.096  -0.56  -3.8e-01  -2.5e-01      4330      2963    1.0
 #> 
 #> Samples were drawn using hmc with nuts.
-#> For each parameter, N_Eff is a crude measure of effective sample size,
-#> and R_hat is the potential scale reduction factor on split chains (at 
-#> convergence, R_hat=1).
+#> For each parameter, ESS_bulk and ESS_tail measure the effective sample size for the entire sample (bulk) and for the .05 and .95 tails (tail), 
+#> and R_hat measures the potential scale reduction on split chains. At convergence R_hat will be very close to 1.00.
 # }
 
 
    
diff --git a/docs/reference/fit-method-code.html b/docs/reference/fit-method-code.html
index cdf0e851d..c8190b196 100644
--- a/docs/reference/fit-method-code.html
+++ b/docs/reference/fit-method-code.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-constrain_variables.html b/docs/reference/fit-method-constrain_variables.html
index ed77fc8a5..16dac657c 100644
--- a/docs/reference/fit-method-constrain_variables.html
+++ b/docs/reference/fit-method-constrain_variables.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-diagnostic_summary.html b/docs/reference/fit-method-diagnostic_summary.html
index a3208753e..d7d13b7c6 100644
--- a/docs/reference/fit-method-diagnostic_summary.html
+++ b/docs/reference/fit-method-diagnostic_summary.html
@@ -24,7 +24,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -161,33 +161,33 @@ 
    See also
    
     
    Examples
    
     
    # \dontrun{
 fit <- cmdstanr_example("schools")
-#> Warning: 235 of 4000 (6.0%) transitions ended with a divergence.
+#> Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 #> Warning: 2 of 4 chains had an E-BFMI less than 0.3.
 #> See https://mc-stan.org/misc/warnings for details.
 fit$diagnostic_summary()
-#> Warning: 235 of 4000 (6.0%) transitions ended with a divergence.
+#> Warning: 191 of 4000 (5.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 #> Warning: 2 of 4 chains had an E-BFMI less than 0.3.
 #> See https://mc-stan.org/misc/warnings for details.
 #> $num_divergent
-#> [1] 104  30  98   3
+#> [1]  23 107  22  39
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.4564907 0.4041931 0.2577905 0.2642813
+#> [1] 0.4160269 0.2285873 0.3803260 0.2845859
 #> 
 fit$diagnostic_summary(quiet = TRUE)
 #> $num_divergent
-#> [1] 104  30  98   3
+#> [1]  23 107  22  39
 #> 
 #> $num_max_treedepth
 #> [1] 0 0 0 0
 #> 
 #> $ebfmi
-#> [1] 0.4564907 0.4041931 0.2577905 0.2642813
+#> [1] 0.4160269 0.2285873 0.3803260 0.2845859
 #> 
 # }
 
diff --git a/docs/reference/fit-method-draws-1.png b/docs/reference/fit-method-draws-1.png
index b241904b2..79a6eb1cf 100644
Binary files a/docs/reference/fit-method-draws-1.png and b/docs/reference/fit-method-draws-1.png differ
diff --git a/docs/reference/fit-method-draws-2.png b/docs/reference/fit-method-draws-2.png
index b9abe3988..3248606ab 100644
Binary files a/docs/reference/fit-method-draws-2.png and b/docs/reference/fit-method-draws-2.png differ
diff --git a/docs/reference/fit-method-draws.html b/docs/reference/fit-method-draws.html
index c7816907b..c11a2ea61 100644
--- a/docs/reference/fit-method-draws.html
+++ b/docs/reference/fit-method-draws.html
@@ -22,7 +22,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -203,7 +203,7 @@ 
    Examples
    
 dim(draws)
 #> [1] 1000    4  105
 str(draws)
-#>  'draws_array' num [1:1000, 1:4, 1:105] -65 -66.8 -64.9 -66.9 -66.5 ...
+#>  'draws_array' num [1:1000, 1:4, 1:105] -64.4 -64.5 -65.8 -66.7 -66 ...
 #>  - attr(*, "dimnames")=List of 3
 #>   ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
 #>   ..$ chain    : chr [1:4] "1" "2" "3" "4"
@@ -214,13 +214,13 @@ 
    Examples
    
 head(posterior::as_draws_matrix(draws))
 #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
 #>     variable
-#> draw lp__  alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#>    1  -65  0.363   -0.70   -0.26    1.04      -0.53      -0.24      -0.40
-#>    2  -67  0.522   -0.55   -0.61    1.18      -0.54      -0.30      -0.65
-#>    3  -65  0.607   -0.57   -0.41    0.82      -0.46      -0.44      -0.49
-#>    4  -67 -0.012   -0.52   -0.11    0.25      -0.65      -0.44      -0.59
-#>    5  -67  0.715   -0.77   -0.34    1.14      -0.41      -0.29      -0.32
-#>    6  -65  0.473   -0.42   -0.41    0.76      -0.52      -0.45      -0.60
+#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
+#>    1  -64  0.37   -0.73   -0.28    0.46      -0.49      -0.47      -0.49
+#>    2  -65  0.37   -0.54   -0.24    0.89      -0.54      -0.31      -0.44
+#>    3  -66  0.44   -1.06   -0.43    0.84      -0.46      -0.30      -0.44
+#>    4  -67  0.49   -0.81    0.15    0.92      -0.42      -0.24      -0.16
+#>    5  -66  0.34   -0.51   -0.53    0.30      -0.55      -0.67      -0.87
+#>    6  -66  0.38   -0.42   -0.44    0.34      -0.54      -0.66      -0.76
 #> # ... with 97 more variables
 
 # or can specify 'format' argument to avoid manual conversion
@@ -229,13 +229,13 @@ 
    Examples
    
 head(draws)
 #> # A draws_matrix: 6 iterations, 1 chains, and 105 variables
 #>     variable
-#> draw lp__  alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
-#>    1  -65  0.363   -0.70   -0.26    1.04      -0.53      -0.24      -0.40
-#>    2  -67  0.522   -0.55   -0.61    1.18      -0.54      -0.30      -0.65
-#>    3  -65  0.607   -0.57   -0.41    0.82      -0.46      -0.44      -0.49
-#>    4  -67 -0.012   -0.52   -0.11    0.25      -0.65      -0.44      -0.59
-#>    5  -67  0.715   -0.77   -0.34    1.14      -0.41      -0.29      -0.32
-#>    6  -65  0.473   -0.42   -0.41    0.76      -0.52      -0.45      -0.60
+#> draw lp__ alpha beta[1] beta[2] beta[3] log_lik[1] log_lik[2] log_lik[3]
+#>    1  -64  0.37   -0.73   -0.28    0.46      -0.49      -0.47      -0.49
+#>    2  -65  0.37   -0.54   -0.24    0.89      -0.54      -0.31      -0.44
+#>    3  -66  0.44   -1.06   -0.43    0.84      -0.46      -0.30      -0.44
+#>    4  -67  0.49   -0.81    0.15    0.92      -0.42      -0.24      -0.16
+#>    5  -66  0.34   -0.51   -0.53    0.30      -0.55      -0.67      -0.87
+#>    6  -66  0.38   -0.42   -0.44    0.34      -0.54      -0.66      -0.76
 #> # ... with 97 more variables
 
 # can select specific parameters
@@ -244,12 +244,12 @@ 
    Examples
    
 #> , , variable = alpha
 #> 
 #>          chain
-#> iteration      1    2    3       4
-#>         1  0.363 0.52 0.50 -0.0071
-#>         2  0.522 0.50 0.46  0.3258
-#>         3  0.607 0.19 0.25  0.2788
-#>         4 -0.012 0.13 0.41  0.2299
-#>         5  0.715 0.48 0.40  0.6308
+#> iteration    1     2     3    4
+#>         1 0.37 0.152 0.089 0.26
+#>         2 0.37 0.301 0.658 0.22
+#>         3 0.44 0.685 0.554 0.52
+#>         4 0.49 0.049 0.734 0.51
+#>         5 0.34 0.171 0.797 0.22
 #> 
 #> # ... with 995 more iterations
 fit$draws("beta")  # selects entire vector beta
@@ -258,31 +258,31 @@ 
    Examples
    
 #> 
 #>          chain
 #> iteration     1     2     3     4
-#>         1 -0.70 -0.74 -0.87 -0.37
-#>         2 -0.55 -0.71 -0.58 -0.84
-#>         3 -0.57 -0.85 -0.59 -0.37
-#>         4 -0.52 -0.66 -0.60 -0.53
-#>         5 -0.77 -0.40 -0.55 -0.68
+#>         1 -0.73 -0.57 -0.63 -0.95
+#>         2 -0.54 -0.63 -0.67 -0.64
+#>         3 -1.06 -0.85 -1.14 -0.59
+#>         4 -0.81 -0.58 -0.70 -0.67
+#>         5 -0.51 -0.60 -0.68 -1.30
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration     1     2     3      4
-#>         1 -0.26 -0.12 -0.28 -0.474
-#>         2 -0.61 -0.12 -0.51 -0.487
-#>         3 -0.41 -0.10 -0.22 -0.069
-#>         4 -0.11 -0.14 -0.13 -0.094
-#>         5 -0.34 -0.08 -0.58 -0.328
+#> iteration     1      2      3     4
+#>         1 -0.28 -0.615 -0.432 -0.21
+#>         2 -0.24 -0.304 -0.049 -0.14
+#>         3 -0.43 -0.431 -0.395  0.06
+#>         4  0.15 -0.192 -0.265 -0.13
+#>         5 -0.53 -0.018 -0.289 -0.19
 #> 
 #> , , variable = beta[3]
 #> 
 #>          chain
 #> iteration    1    2    3    4
-#>         1 1.04 0.96 0.78 0.75
-#>         2 1.18 0.90 0.40 0.81
-#>         3 0.82 0.29 1.27 0.64
-#>         4 0.25 0.45 0.97 0.64
-#>         5 1.14 0.18 0.54 0.66
+#>         1 0.46 0.87 0.53 0.50
+#>         2 0.89 0.84 0.89 0.22
+#>         3 0.84 0.83 0.47 0.35
+#>         4 0.92 0.71 1.36 0.67
+#>         5 0.30 0.42 1.40 0.59
 #> 
 #> # ... with 995 more iterations
 fit$draws(c("alpha", "beta[2]"))
@@ -290,22 +290,22 @@ 
    Examples
    
 #> , , variable = alpha
 #> 
 #>          chain
-#> iteration      1    2    3       4
-#>         1  0.363 0.52 0.50 -0.0071
-#>         2  0.522 0.50 0.46  0.3258
-#>         3  0.607 0.19 0.25  0.2788
-#>         4 -0.012 0.13 0.41  0.2299
-#>         5  0.715 0.48 0.40  0.6308
+#> iteration    1     2     3    4
+#>         1 0.37 0.152 0.089 0.26
+#>         2 0.37 0.301 0.658 0.22
+#>         3 0.44 0.685 0.554 0.52
+#>         4 0.49 0.049 0.734 0.51
+#>         5 0.34 0.171 0.797 0.22
 #> 
 #> , , variable = beta[2]
 #> 
 #>          chain
-#> iteration     1     2     3      4
-#>         1 -0.26 -0.12 -0.28 -0.474
-#>         2 -0.61 -0.12 -0.51 -0.487
-#>         3 -0.41 -0.10 -0.22 -0.069
-#>         4 -0.11 -0.14 -0.13 -0.094
-#>         5 -0.34 -0.08 -0.58 -0.328
+#> iteration     1      2      3     4
+#>         1 -0.28 -0.615 -0.432 -0.21
+#>         2 -0.24 -0.304 -0.049 -0.14
+#>         3 -0.43 -0.431 -0.395  0.06
+#>         4  0.15 -0.192 -0.265 -0.13
+#>         5 -0.53 -0.018 -0.289 -0.19
 #> 
 #> # ... with 995 more iterations
 
@@ -323,21 +323,21 @@ 
    Examples
    
 #> # A draws_matrix: 6 iterations, 1 chains, and 3 variables
 #>     variable
 #> draw beta[1] beta[2] beta[3]
-#>    1   -0.80   -0.25    0.98
-#>    2   -0.56   -0.31    0.57
-#>    3   -0.98   -0.35    0.66
-#>    4   -0.67   -0.78    0.49
-#>    5   -0.98   -0.38    0.75
-#>    6   -0.52   -0.52    0.37
+#>    1   -0.70   -0.15   0.690
+#>    2   -0.69   -0.54   0.897
+#>    3   -0.41   -0.67   0.323
+#>    4   -0.93   -0.51   0.460
+#>    5   -0.77   -0.39   0.031
+#>    6   -0.64   -0.16   0.711
 head(fit$draws("beta", format = "df"))
 #> # A draws_df: 6 iterations, 1 chains, and 3 variables
 #>   beta[1] beta[2] beta[3]
-#> 1   -0.80   -0.25    0.98
-#> 2   -0.56   -0.31    0.57
-#> 3   -0.98   -0.35    0.66
-#> 4   -0.67   -0.78    0.49
-#> 5   -0.98   -0.38    0.75
-#> 6   -0.52   -0.52    0.37
+#> 1   -0.70   -0.15   0.690
+#> 2   -0.69   -0.54   0.897
+#> 3   -0.41   -0.67   0.323
+#> 4   -0.93   -0.51   0.460
+#> 5   -0.77   -0.39   0.031
+#> 6   -0.64   -0.16   0.711
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 # }
 
diff --git a/docs/reference/fit-method-grad_log_prob.html b/docs/reference/fit-method-grad_log_prob.html
index 93ab582f4..d301b29fd 100644
--- a/docs/reference/fit-method-grad_log_prob.html
+++ b/docs/reference/fit-method-grad_log_prob.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-gradients.html b/docs/reference/fit-method-gradients.html
index 3e3e1e00e..c331800f7 100644
--- a/docs/reference/fit-method-gradients.html
+++ b/docs/reference/fit-method-gradients.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -132,10 +132,10 @@ 
    Examples
    
 # retrieve the gradients
 test$gradients()
 #>   param_idx     value     model finite_diff        error
-#> 1         0 -1.816810  37.64170    37.64170 -8.16570e-10
-#> 2         1  0.376746 -18.33100   -18.33100 -3.03892e-08
-#> 3         2  0.477942 -15.36460   -15.36460 -2.99484e-08
-#> 4         3  1.852310  -7.94121    -7.94121 -3.99522e-08
+#> 1         0 -1.956090  42.51440    42.51440 -1.09401e-08
+#> 2         1 -0.130427 -13.33070   -13.33070 -1.40895e-08
+#> 3         2  1.228760 -24.25270   -24.25270 -4.45662e-08
+#> 4         3  0.625050   3.12825     3.12825 -3.33963e-09
 # }
 
 
    
diff --git a/docs/reference/fit-method-hessian.html b/docs/reference/fit-method-hessian.html
index 0e80242bd..4cc7fea86 100644
--- a/docs/reference/fit-method-hessian.html
+++ b/docs/reference/fit-method-hessian.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-init.html b/docs/reference/fit-method-init.html
index 10e3fac7a..4d0449d46 100644
--- a/docs/reference/fit-method-init.html
+++ b/docs/reference/fit-method-init.html
@@ -22,7 +22,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -140,11 +140,11 @@ 
    Examples
    
 str(fit$init())
 #> List of 2
 #>  $ :List of 2
-#>   ..$ alpha: num -1.12
-#>   ..$ beta : num [1:3] 0.398 0.421 -0.425
+#>   ..$ alpha: num -1.19
+#>   ..$ beta : num [1:3] -0.0532 0.2552 1.706
 #>  $ :List of 2
-#>   ..$ alpha: num -0.877
-#>   ..$ beta : num [1:3] 1.3 -1.34 1.43
+#>   ..$ alpha: num 1
+#>   ..$ beta : num [1:3] -0.496 0.356 -1.135
 
 # partial inits (only specifying for a subset of parameters)
 init_list <- list(
diff --git a/docs/reference/fit-method-init_model_methods.html b/docs/reference/fit-method-init_model_methods.html
index 83a00fb4e..a34a009bd 100644
--- a/docs/reference/fit-method-init_model_methods.html
+++ b/docs/reference/fit-method-init_model_methods.html
@@ -24,7 +24,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-inv_metric.html b/docs/reference/fit-method-inv_metric.html
index 56268463d..1a8c8a7e2 100644
--- a/docs/reference/fit-method-inv_metric.html
+++ b/docs/reference/fit-method-inv_metric.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -139,76 +139,76 @@ 
    Examples
    
 fit <- cmdstanr_example("logistic")
 fit$inv_metric()
 #> $`1`
-#>          [,1]      [,2]      [,3]      [,4]
-#> [1,] 0.041261 0.0000000 0.0000000 0.0000000
-#> [2,] 0.000000 0.0524298 0.0000000 0.0000000
-#> [3,] 0.000000 0.0000000 0.0481435 0.0000000
-#> [4,] 0.000000 0.0000000 0.0000000 0.0732764
+#>           [,1]      [,2]     [,3]      [,4]
+#> [1,] 0.0399675 0.0000000 0.000000 0.0000000
+#> [2,] 0.0000000 0.0581864 0.000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.045564 0.0000000
+#> [4,] 0.0000000 0.0000000 0.000000 0.0657117
 #> 
 #> $`2`
-#>           [,1]      [,2]     [,3]      [,4]
-#> [1,] 0.0476774 0.0000000 0.000000 0.0000000
-#> [2,] 0.0000000 0.0504326 0.000000 0.0000000
-#> [3,] 0.0000000 0.0000000 0.052821 0.0000000
-#> [4,] 0.0000000 0.0000000 0.000000 0.0711489
+#>          [,1]      [,2]      [,3]      [,4]
+#> [1,] 0.042812 0.0000000 0.0000000 0.0000000
+#> [2,] 0.000000 0.0677379 0.0000000 0.0000000
+#> [3,] 0.000000 0.0000000 0.0505126 0.0000000
+#> [4,] 0.000000 0.0000000 0.0000000 0.0732512
 #> 
 #> $`3`
 #>           [,1]     [,2]      [,3]      [,4]
-#> [1,] 0.0465449 0.000000 0.0000000 0.0000000
-#> [2,] 0.0000000 0.058276 0.0000000 0.0000000
-#> [3,] 0.0000000 0.000000 0.0462442 0.0000000
-#> [4,] 0.0000000 0.000000 0.0000000 0.0746648
+#> [1,] 0.0421817 0.000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.062998 0.0000000 0.0000000
+#> [3,] 0.0000000 0.000000 0.0483093 0.0000000
+#> [4,] 0.0000000 0.000000 0.0000000 0.0806989
 #> 
 #> $`4`
-#>          [,1]      [,2]     [,3]      [,4]
-#> [1,] 0.043908 0.0000000 0.000000 0.0000000
-#> [2,] 0.000000 0.0657091 0.000000 0.0000000
-#> [3,] 0.000000 0.0000000 0.048942 0.0000000
-#> [4,] 0.000000 0.0000000 0.000000 0.0632482
+#>           [,1]      [,2]      [,3]      [,4]
+#> [1,] 0.0458222 0.0000000 0.0000000 0.0000000
+#> [2,] 0.0000000 0.0724218 0.0000000 0.0000000
+#> [3,] 0.0000000 0.0000000 0.0524932 0.0000000
+#> [4,] 0.0000000 0.0000000 0.0000000 0.0726335
 #> 
 fit$inv_metric(matrix=FALSE)
 #> $`1`
-#> [1] 0.0412610 0.0524298 0.0481435 0.0732764
+#> [1] 0.0399675 0.0581864 0.0455640 0.0657117
 #> 
 #> $`2`
-#> [1] 0.0476774 0.0504326 0.0528210 0.0711489
+#> [1] 0.0428120 0.0677379 0.0505126 0.0732512
 #> 
 #> $`3`
-#> [1] 0.0465449 0.0582760 0.0462442 0.0746648
+#> [1] 0.0421817 0.0629980 0.0483093 0.0806989
 #> 
 #> $`4`
-#> [1] 0.0439080 0.0657091 0.0489420 0.0632482
+#> [1] 0.0458222 0.0724218 0.0524932 0.0726335
 #> 
 
 fit <- cmdstanr_example("logistic", metric = "dense_e")
 fit$inv_metric()
 #> $`1`
-#>             [,1]        [,2]        [,3]        [,4]
-#> [1,]  0.05192450 -0.00808928  0.00233609  0.00537011
-#> [2,] -0.00808928  0.05617200 -0.00192090 -0.00711062
-#> [3,]  0.00233609 -0.00192090  0.04964080 -0.01391690
-#> [4,]  0.00537011 -0.00711062 -0.01391690  0.07126540
+#>              [,1]         [,2]        [,3]         [,4]
+#> [1,]  0.048423300  0.001823680  0.00659113 -0.000666867
+#> [2,]  0.001823680  0.055877400 -0.00378891  0.000328776
+#> [3,]  0.006591130 -0.003788910  0.04607360 -0.010493400
+#> [4,] -0.000666867  0.000328776 -0.01049340  0.061314400
 #> 
 #> $`2`
-#>             [,1]         [,2]        [,3]        [,4]
-#> [1,] 0.044604400  0.000608339  0.00107581  0.00451756
-#> [2,] 0.000608339  0.058117400 -0.01088670 -0.00141863
-#> [3,] 0.001075810 -0.010886700  0.04437890 -0.00650639
-#> [4,] 0.004517560 -0.001418630 -0.00650639  0.06984850
+#>              [,1]         [,2]         [,3]        [,4]
+#> [1,]  0.055615500 -0.004688890  0.000366103  0.00657168
+#> [2,] -0.004688890  0.070634600  0.000161933 -0.00900785
+#> [3,]  0.000366103  0.000161933  0.043863800 -0.01242910
+#> [4,]  0.006571680 -0.009007850 -0.012429100  0.06169190
 #> 
 #> $`3`
-#>             [,1]         [,2]         [,3]        [,4]
-#> [1,]  0.04180740 -0.001275980  0.005750270  0.00305686
-#> [2,] -0.00127598  0.054490900 -0.000483551 -0.00770396
-#> [3,]  0.00575027 -0.000483551  0.044247700 -0.01478120
-#> [4,]  0.00305686 -0.007703960 -0.014781200  0.06501070
+#>              [,1]         [,2]         [,3]        [,4]
+#> [1,]  0.046092000  0.000277152 -0.000196182  0.00653545
+#> [2,]  0.000277152  0.052935700 -0.001316380 -0.00135849
+#> [3,] -0.000196182 -0.001316380  0.052723400 -0.00738233
+#> [4,]  0.006535450 -0.001358490 -0.007382330  0.06829300
 #> 
 #> $`4`
-#>              [,1]         [,2]         [,3]        [,4]
-#> [1,]  4.17467e-02 -0.000269914  4.51726e-05  0.00472656
-#> [2,] -2.69914e-04  0.061160800 -1.36053e-02 -0.00637189
-#> [3,]  4.51726e-05 -0.013605300  4.96363e-02 -0.00759941
-#> [4,]  4.72656e-03 -0.006371890 -7.59941e-03  0.06762740
+#>             [,1]        [,2]        [,3]        [,4]
+#> [1,]  0.05362400 -0.00182030  0.00998512 -0.00383164
+#> [2,] -0.00182030  0.06051710  0.00414984 -0.01575160
+#> [3,]  0.00998512  0.00414984  0.04282750 -0.01094520
+#> [4,] -0.00383164 -0.01575160 -0.01094520  0.07697140
 #> 
 # }
 
diff --git a/docs/reference/fit-method-log_prob.html b/docs/reference/fit-method-log_prob.html
index 74b7e2f25..de3159dd8 100644
--- a/docs/reference/fit-method-log_prob.html
+++ b/docs/reference/fit-method-log_prob.html
@@ -18,7 +18,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
diff --git a/docs/reference/fit-method-loo.html b/docs/reference/fit-method-loo.html
index dcb5e515f..30281bc5c 100644
--- a/docs/reference/fit-method-loo.html
+++ b/docs/reference/fit-method-loo.html
@@ -21,7 +21,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -122,10 +122,10 @@ 
    Leave-one-out cross-validation (LOO-CV)
    
     
    
     
    Arguments
    
     
    variables
    
-
    (character vector) The name(s) of the variable(s) in the
-Stan program containing the pointwise log-likelihood. The default is to
-look for "log_lik". This argument is passed to the
-$draws() method.
    
+
    (string) The name of the variable in the Stan program
+containing the pointwise log-likelihood. The default is to look for
+"log_lik". This argument is passed to the $draws()
+method.
    
 
 
 
    r_eff
    
@@ -184,7 +184,7 @@ 
    Examples
    
 #>          Estimate  SE
 #> elpd_loo    -63.7 4.1
 #> p_loo         3.9 0.5
-#> looic       127.4 8.2
+#> looic       127.4 8.3
 #> ------
 #> MCSE of elpd_loo is 0.0.
 #> MCSE and ESS estimates assume MCMC draws (r_eff in [0.9, 1.4]).
diff --git a/docs/reference/fit-method-lp-1.png b/docs/reference/fit-method-lp-1.png
index 7016ed34e..54cfc9139 100644
Binary files a/docs/reference/fit-method-lp-1.png and b/docs/reference/fit-method-lp-1.png differ
diff --git a/docs/reference/fit-method-lp.html b/docs/reference/fit-method-lp.html
index 180e4c9a9..4ee587d71 100644
--- a/docs/reference/fit-method-lp.html
+++ b/docs/reference/fit-method-lp.html
@@ -5,8 +5,8 @@
 the posterior is available via the $lp_approx() method. For
 Laplace approximation the unnormalized density of the approximation to
 the posterior is available via the $lp_approx() method.
-See the Log Probability Increment vs. Sampling Statement
-section of the Stan Reference Manual for details on when normalizing
+See the Increment log density and Distribution Statements
+sections of the Stan Reference Manual for details on when normalizing
 constants are dropped from log probability calculations.">Extract (penalized) maximum likelihood estimate after optimization — fit-method-mle • cmdstanrExtract point estimate after optimization — fit-method-mle • cmdstanr
@@ -23,7 +27,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
       
     
    
 
@@ -104,19 +108,23 @@
       
    
   
    
     
    
-    
    Extract (penalized) maximum likelihood estimate after optimization
    
+    
    Extract point estimate after optimization
    
     Source: R/fit.R
     
     
    
 
     
    
-    
    The $mle() method is only available for CmdStanMLE objects.
-It returns the penalized maximum likelihood estimate (posterior mode) as a
-numeric vector with one element per variable. The returned vector does not
-include lp__, the total log probability (target) accumulated in the
-model block of the Stan program, which is available via the
-$lp() method and also included in the
-$draws() method.
    
+    
    The $mle() method is only available for CmdStanMLE
+objects. It returns the point estimate as a numeric vector with one element
+per variable. The returned vector does not include lp__, the total log
+probability (target) accumulated in the model block of the Stan program,
+which is available via the $lp() method and also
+included in the $draws() method.
    
+
    For models with constrained parameters that are fit with jacobian=TRUE,
+the $mle() method actually returns the maximum a posteriori (MAP)
+estimate (posterior mode) rather than the MLE. See
+$optimize() and the CmdStan User's Guide for
+more details.
    
     
    
 
     
    
@@ -148,13 +156,13 @@ 
    Examples
    
 fit <- cmdstanr_example("logistic", method = "optimize")
 fit$mle("alpha")
 #>    alpha 
-#> 0.364419 
+#> 0.364453 
 fit$mle("beta")
 #>   beta[1]   beta[2]   beta[3] 
-#> -0.631543 -0.258962  0.648511 
+#> -0.631550 -0.258968  0.648499 
 fit$mle("beta[2]")
 #>   beta[2] 
-#> -0.258962 
+#> -0.258968 
 # }
 
 
    
diff --git a/docs/reference/fit-method-num_chains.html b/docs/reference/fit-method-num_chains.html
index 0cce33e50..020f9be41 100644
--- a/docs/reference/fit-method-num_chains.html
+++ b/docs/reference/fit-method-num_chains.html
@@ -17,7 +17,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0

    diff --git a/docs/reference/fit-method-output.html b/docs/reference/fit-method-output.html index 9d6e90765..641421f36 100644 --- a/docs/reference/fit-method-output.html +++ b/docs/reference/fit-method-output.html @@ -22,7 +22,7 @@ cmdstanr - 0.8.1 + 0.9.0

    @@ -167,22 +167,22 @@

    Examples

    #> num_chains = 1 (Default) #> id = 1 (Default) #> data -#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpBGNQRs/temp_libpath45223306cea5/cmdstanr/logistic.data.json +#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp0Jp8Zk/temp_libpathb838752e97c0/cmdstanr/logistic.data.json #> init = 2 (Default) #> random -#> seed = 1162623978 +#> seed = 279613479 #> output -#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021538-1-35ac7c.csv +#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-1-28c1ee.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) -#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-profile-202407021538-1-88cb05.csv +#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-profile-202503310845-1-0139e1.csv #> save_cmdstan_config = false (Default) #> num_threads = 1 (Default) #> #> -#> Gradient evaluation took 5.1e-05 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds. +#> Gradient evaluation took 2.7e-05 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds. #> Adjust your expectations accordingly! #> #> @@ -209,9 +209,9 @@

    Examples

    #> Iteration: 1900 / 2000 [ 95%] (Sampling) #> Iteration: 2000 / 2000 [100%] (Sampling) #> -#> Elapsed Time: 0.028 seconds (Warm-up) -#> 0.097 seconds (Sampling) -#> 0.125 seconds (Total) +#> Elapsed Time: 0.022 seconds (Warm-up) +#> 0.074 seconds (Sampling) +#> 0.096 seconds (Total) out <- fit_mcmc$output() str(out) #> List of 4 @@ -239,22 +239,22 @@

    Examples

    #> save_iterations = false (Default) #> id = 1 (Default) #> data -#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpBGNQRs/temp_libpath45223306cea5/cmdstanr/logistic.data.json +#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp0Jp8Zk/temp_libpathb838752e97c0/cmdstanr/logistic.data.json #> init = 2 (Default) #> random -#> seed = 212160042 +#> seed = 384468259 #> output -#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021538-1-18b979.csv +#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-1-819b35.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) -#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-profile-202407021538-1-3c00bf.csv +#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-profile-202503310845-1-053c3a.csv #> save_cmdstan_config = false (Default) #> num_threads = 1 (Default) #> -#> Initial log joint probability = -125.672 +#> Initial log joint probability = -105.906 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 7 -63.9218 0.000372447 0.00126853 1 1 10 +#> 8 -63.9218 6.03661e-05 0.000635963 0.6801 0.6801 10 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance @@ -277,16 +277,16 @@

    Examples

    #> output_samples = 1000 (Default) #> id = 1 (Default) #> data -#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpBGNQRs/temp_libpath45223306cea5/cmdstanr/logistic.data.json +#> file = /private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp0Jp8Zk/temp_libpathb838752e97c0/cmdstanr/logistic.data.json #> init = 2 (Default) #> random -#> seed = 490142728 +#> seed = 428491960 #> output -#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021538-1-81868f.csv +#> file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-1-1320f3.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) -#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-profile-202407021538-1-385cb7.csv +#> profile_file = /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-profile-202503310845-1-101778.csv #> save_cmdstan_config = false (Default) #> num_threads = 1 (Default) #> @@ -298,8 +298,8 @@

    Examples

    #> #> #> -#> Gradient evaluation took 2.5e-05 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds. +#> Gradient evaluation took 2.7e-05 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds. #> Adjust your expectations accordingly! #> #> @@ -313,9 +313,9 @@

    Examples

    #> #> Begin stochastic gradient ascent. #> iter ELBO delta_ELBO_mean delta_ELBO_med notes -#> 100 -66.510 1.000 1.000 -#> 200 -66.168 0.503 1.000 -#> 300 -66.153 0.335 0.005 MEDIAN ELBO CONVERGED +#> 100 -67.030 1.000 1.000 +#> 200 -66.410 0.505 1.000 +#> 300 -65.913 0.339 0.009 MEDIAN ELBO CONVERGED #> #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. diff --git a/docs/reference/fit-method-profiles.html b/docs/reference/fit-method-profiles.html index 3cde575ed..325bafed8 100644 --- a/docs/reference/fit-method-profiles.html +++ b/docs/reference/fit-method-profiles.html @@ -21,7 +21,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -176,35 +176,35 @@

    Examples

    fit$profiles() #> [[1]] #> name thread_id total_time forward_time reverse_time chain_stack -#> 1 gq 0x7ff858f85100 0.000442373 0.000442373 0.000000000 0 -#> 2 likelihood 0x7ff858f85100 0.001250430 0.000907142 0.000343284 6721 +#> 1 likelihood 0x7ff85af4eb00 0.001158940 0.000848855 0.00031008 6721 +#> 2 gq 0x7ff85af4eb00 0.000444795 0.000444795 0.00000000 0 #> no_chain_stack autodiff_calls no_autodiff_calls -#> 1 0 0 1000 -#> 2 6721 6721 1 +#> 1 6721 6721 1 +#> 2 0 0 1000 #> #> [[2]] #> name thread_id total_time forward_time reverse_time chain_stack -#> 1 gq 0x7ff858f85100 0.000468161 0.000468161 0.000000000 0 -#> 2 likelihood 0x7ff858f85100 0.001255240 0.000911108 0.000344131 6792 +#> 1 likelihood 0x7ff85af4eb00 0.001207200 0.000885927 0.000321272 6792 +#> 2 gq 0x7ff85af4eb00 0.000453808 0.000453808 0.000000000 0 #> no_chain_stack autodiff_calls no_autodiff_calls -#> 1 0 0 1000 -#> 2 6792 6792 1 +#> 1 6792 6792 1 +#> 2 0 0 1000 #> #> [[3]] #> name thread_id total_time forward_time reverse_time chain_stack -#> 1 gq 0x7ff858f85100 0.000458935 0.000458935 0.000000000 0 -#> 2 likelihood 0x7ff858f85100 0.001356490 0.000992885 0.000363606 7163 +#> 1 likelihood 0x7ff85af4eb00 0.001181750 0.000860407 0.000321346 6835 +#> 2 gq 0x7ff85af4eb00 0.000418544 0.000418544 0.000000000 0 #> no_chain_stack autodiff_calls no_autodiff_calls -#> 1 0 0 1000 -#> 2 7163 7163 1 +#> 1 6835 6835 1 +#> 2 0 0 1000 #> #> [[4]] #> name thread_id total_time forward_time reverse_time chain_stack -#> 1 gq 0x7ff858f85100 0.000489173 0.000489173 0.000000000 0 -#> 2 likelihood 0x7ff858f85100 0.001301980 0.000949488 0.000352494 6979 +#> 1 likelihood 0x7ff85af4eb00 0.001272150 0.000927098 0.000345053 6955 +#> 2 gq 0x7ff85af4eb00 0.000416536 0.000416536 0.000000000 0 #> no_chain_stack autodiff_calls no_autodiff_calls -#> 1 0 0 1000 -#> 2 6979 6979 1 +#> 1 6955 6955 1 +#> 2 0 0 1000 #> # } diff --git a/docs/reference/fit-method-return_codes.html b/docs/reference/fit-method-return_codes.html index ad9659f78..8c06616cf 100644 --- a/docs/reference/fit-method-return_codes.html +++ b/docs/reference/fit-method-return_codes.html @@ -18,7 +18,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -130,9 +130,7 @@

    Examples

    # \dontrun{
 # example with return codes all zero
 fit_mcmc <- cmdstanr_example("schools", method = "sample")
-#> Warning: 127 of 4000 (3.0%) transitions ended with a divergence.
-#> See https://mc-stan.org/misc/warnings for details.
-#> Warning: 2 of 4 chains had an E-BFMI less than 0.3.
+#> Warning: 81 of 4000 (2.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 fit_mcmc$return_codes() # should be all zero
 #> [1] 0 0 0 0
diff --git a/docs/reference/fit-method-sampler_diagnostics.html b/docs/reference/fit-method-sampler_diagnostics.html
index b870781b3..104338f30 100644
--- a/docs/reference/fit-method-sampler_diagnostics.html
+++ b/docs/reference/fit-method-sampler_diagnostics.html
@@ -20,7 +20,7 @@
       
       
         cmdstanr
-        0.8.1
+        0.9.0
    @@ -152,7 +152,7 @@

    Examples

    fit <- cmdstanr_example("logistic") sampler_diagnostics <- fit$sampler_diagnostics() str(sampler_diagnostics) -#> 'draws_array' num [1:1000, 1:4, 1:6] 3 3 2 3 2 3 2 2 3 2 ... +#> 'draws_array' num [1:1000, 1:4, 1:6] 2 2 2 2 2 2 2 2 3 2 ... #> - attr(*, "dimnames")=List of 3 #> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> ..$ chain : chr [1:4] "1" "2" "3" "4" @@ -162,16 +162,16 @@

    Examples

    as_draws_df(sampler_diagnostics) #> # A draws_df: 1000 iterations, 4 chains, and 6 variables #> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__ -#> 1 3 0 68 0.91 0.83 7 -#> 2 3 0 66 1.00 0.83 7 -#> 3 2 0 66 0.89 0.83 3 -#> 4 3 0 66 1.00 0.83 7 -#> 5 2 0 67 0.95 0.83 3 -#> 6 3 0 69 0.85 0.83 7 -#> 7 2 0 66 1.00 0.83 3 -#> 8 2 0 66 0.83 0.83 3 -#> 9 3 0 65 0.94 0.83 7 -#> 10 2 0 66 0.93 0.83 7 +#> 1 2 0 69 0.87 0.79 3 +#> 2 2 0 67 0.96 0.79 7 +#> 3 2 0 68 0.80 0.79 3 +#> 4 2 0 69 0.72 0.79 3 +#> 5 2 0 68 0.83 0.79 3 +#> 6 2 0 69 0.94 0.79 3 +#> 7 2 0 66 0.99 0.79 3 +#> 8 2 0 68 0.62 0.79 3 +#> 9 3 0 67 1.00 0.79 7 +#> 10 2 0 67 0.99 0.79 7 #> # ... with 3990 more draws #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'} @@ -179,16 +179,16 @@

    Examples

    fit$sampler_diagnostics(format = "df") #> # A draws_df: 1000 iterations, 4 chains, and 6 variables #> treedepth__ divergent__ energy__ accept_stat__ stepsize__ n_leapfrog__ -#> 1 3 0 68 0.91 0.83 7 -#> 2 3 0 66 1.00 0.83 7 -#> 3 2 0 66 0.89 0.83 3 -#> 4 3 0 66 1.00 0.83 7 -#> 5 2 0 67 0.95 0.83 3 -#> 6 3 0 69 0.85 0.83 7 -#> 7 2 0 66 1.00 0.83 3 -#> 8 2 0 66 0.83 0.83 3 -#> 9 3 0 65 0.94 0.83 7 -#> 10 2 0 66 0.93 0.83 7 +#> 1 2 0 69 0.87 0.79 3 +#> 2 2 0 67 0.96 0.79 7 +#> 3 2 0 68 0.80 0.79 3 +#> 4 2 0 69 0.72 0.79 3 +#> 5 2 0 68 0.83 0.79 3 +#> 6 2 0 69 0.94 0.79 3 +#> 7 2 0 66 0.99 0.79 3 +#> 8 2 0 68 0.62 0.79 3 +#> 9 3 0 67 1.00 0.79 7 +#> 10 2 0 67 0.99 0.79 7 #> # ... with 3990 more draws #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'} # } diff --git a/docs/reference/fit-method-save_object.html b/docs/reference/fit-method-save_object.html index baf767c76..ac2d20ae2 100644 --- a/docs/reference/fit-method-save_object.html +++ b/docs/reference/fit-method-save_object.html @@ -1,9 +1,12 @@ -Save fitted model object to a file — fit-method-save_object • cmdstanrSave fitted model object to a file — fit-method-save_object • cmdstanr @@ -21,7 +24,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -113,6 +116,9 @@

    Save fitted model object to a file

    model object. Because the contents of the CmdStan output CSV files are only read into R lazily (i.e., as needed), the $save_object() method is the safest way to guarantee that everything has been read in before saving.

    +

    See the "Saving fitted model objects" section of the +Getting started with CmdStanR +vignette for some suggestions on faster model saving for large models.

    @@ -146,18 +152,18 @@

    Examples

    fit <- readRDS(temp_rds_file) fit$summary() #> # A tibble: 105 × 10 -#> variable mean median sd mad q5 q95 rhat ess_bulk -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -66.0 -65.7 1.42 1.27 -68.7 -64.3 1.00 2381. -#> 2 alpha 0.379 0.377 0.223 0.224 0.0200 0.754 1.00 4356. -#> 3 beta[1] -0.663 -0.664 0.252 0.252 -1.08 -0.250 1.00 4356. -#> 4 beta[2] -0.270 -0.263 0.223 0.215 -0.645 0.0924 1.00 4293. -#> 5 beta[3] 0.686 0.676 0.269 0.263 0.261 1.15 1.00 4246. -#> 6 log_lik[1] -0.516 -0.511 0.101 0.101 -0.692 -0.364 1.00 4361. -#> 7 log_lik[2] -0.401 -0.381 0.149 0.144 -0.676 -0.193 1.00 4797. -#> 8 log_lik[3] -0.494 -0.460 0.214 0.203 -0.897 -0.207 1.00 4344. -#> 9 log_lik[4] -0.450 -0.431 0.154 0.150 -0.725 -0.233 1.00 4169. -#> 10 log_lik[5] -1.19 -1.17 0.280 0.279 -1.67 -0.767 1.00 4463. +#> variable mean median sd mad q5 q95 rhat ess_bulk +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -65.9 -65.6 1.42 1.21 -68.6 -64.3 1.00 2184. +#> 2 alpha 0.376 0.374 0.216 0.221 0.0219 0.730 1.00 4114. +#> 3 beta[1] -0.661 -0.659 0.244 0.246 -1.08 -0.269 1.00 4410. +#> 4 beta[2] -0.274 -0.272 0.227 0.228 -0.639 0.0940 1.00 3599. +#> 5 beta[3] 0.675 0.668 0.265 0.261 0.256 1.11 1.00 3811. +#> 6 log_lik[1] -0.517 -0.507 0.0995 0.0970 -0.692 -0.369 1.00 4022. +#> 7 log_lik[2] -0.404 -0.387 0.144 0.134 -0.663 -0.199 1.00 4613. +#> 8 log_lik[3] -0.501 -0.465 0.219 0.208 -0.909 -0.215 1.00 3919. +#> 9 log_lik[4] -0.450 -0.432 0.152 0.148 -0.723 -0.233 1.00 3378. +#> 10 log_lik[5] -1.18 -1.15 0.277 0.272 -1.68 -0.758 1.00 4267. #> # ℹ 95 more rows #> # ℹ 1 more variable: ess_tail <dbl> # } diff --git a/docs/reference/fit-method-save_output_files.html b/docs/reference/fit-method-save_output_files.html index 6b521c25b..06261804b 100644 --- a/docs/reference/fit-method-save_output_files.html +++ b/docs/reference/fit-method-save_output_files.html @@ -24,7 +24,7 @@ cmdstanr - 0.8.1 + 0.9.0
    @@ -224,33 +224,33 @@

    Examples

    # \dontrun{
 fit <- cmdstanr_example()
 fit$output_files()
-#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021539-1-615e63.csv"
-#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021539-2-615e63.csv"
-#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021539-3-615e63.csv"
-#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021539-4-615e63.csv"
+#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-1-466e74.csv"
+#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-2-466e74.csv"
+#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-3-466e74.csv"
+#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310845-4-466e74.csv"
 fit$data_file()
-#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpBGNQRs/temp_libpath45223306cea5/cmdstanr/logistic.data.json"
+#> [1] "/private/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp0Jp8Zk/temp_libpathb838752e97c0/cmdstanr/logistic.data.json"
 
 # just using tempdir for the example
 my_dir <- tempdir()
 fit$save_output_files(dir = my_dir, basename = "banana")
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/banana-202407021539-1-897d5b.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/banana-202407021539-2-897d5b.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/banana-202407021539-3-897d5b.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/banana-202407021539-4-897d5b.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/banana-202503310845-1-6af888.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/banana-202503310845-2-6af888.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/banana-202503310845-3-6af888.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/banana-202503310845-4-6af888.csv
 fit$save_output_files(dir = my_dir, basename = "tomato", timestamp = FALSE)
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/tomato-1-4291b8.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/tomato-2-4291b8.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/tomato-3-4291b8.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/tomato-4-4291b8.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/tomato-1-7df674.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/tomato-2-7df674.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/tomato-3-7df674.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/tomato-4-7df674.csv
 fit$save_output_files(dir = my_dir, basename = "lettuce", timestamp = FALSE, random = FALSE)
 #> Moved 4 files and set internal paths to new locations:
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/lettuce-1.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/lettuce-2.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/lettuce-3.csv
-#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/lettuce-4.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/lettuce-1.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/lettuce-2.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/lettuce-3.csv
+#> - /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/lettuce-4.csv
 # }
    diff --git a/docs/reference/fit-method-summary.html b/docs/reference/fit-method-summary.html index a1ceadd7f..e394ca9ff 100644 --- a/docs/reference/fit-method-summary.html +++ b/docs/reference/fit-method-summary.html @@ -27,7 +27,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -163,53 +163,53 @@

    Examples

    fit <- cmdstanr_example("logistic") fit$summary() #> # A tibble: 105 × 10 -#> variable mean median sd mad q5 q95 rhat ess_bulk -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -66.0 -65.6 1.45 1.26 -68.7 -64.3 1.00 1922. -#> 2 alpha 0.378 0.373 0.221 0.218 0.0220 0.750 1.00 4329. -#> 3 beta[1] -0.669 -0.660 0.252 0.257 -1.09 -0.264 1.00 3807. -#> 4 beta[2] -0.279 -0.273 0.223 0.223 -0.649 0.0786 1.00 3937. -#> 5 beta[3] 0.677 0.669 0.267 0.257 0.248 1.13 1.00 3914. -#> 6 log_lik[1] -0.516 -0.510 0.0985 0.0955 -0.690 -0.361 1.00 4145. -#> 7 log_lik[2] -0.404 -0.386 0.148 0.137 -0.677 -0.198 1.00 4343. -#> 8 log_lik[3] -0.502 -0.466 0.215 0.206 -0.908 -0.213 1.00 4197. -#> 9 log_lik[4] -0.447 -0.432 0.150 0.147 -0.720 -0.233 1.00 3438. -#> 10 log_lik[5] -1.18 -1.16 0.280 0.281 -1.68 -0.753 1.00 4471. +#> variable mean median sd mad q5 q95 rhat ess_bulk +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -66.0 -65.6 1.45 1.21 -68.7 -64.3 1.00 2233. +#> 2 alpha 0.382 0.380 0.214 0.217 0.0308 0.730 1.00 4473. +#> 3 beta[1] -0.667 -0.665 0.248 0.254 -1.08 -0.261 1.00 4454. +#> 4 beta[2] -0.274 -0.271 0.233 0.232 -0.649 0.103 1.00 4166. +#> 5 beta[3] 0.680 0.672 0.264 0.258 0.254 1.12 1.00 3555. +#> 6 log_lik[1] -0.514 -0.506 0.0973 0.0977 -0.684 -0.368 1.00 4272. +#> 7 log_lik[2] -0.403 -0.383 0.147 0.137 -0.677 -0.199 1.00 4474. +#> 8 log_lik[3] -0.497 -0.465 0.217 0.204 -0.897 -0.202 1.00 4228. +#> 9 log_lik[4] -0.450 -0.434 0.152 0.148 -0.717 -0.238 1.00 3834. +#> 10 log_lik[5] -1.18 -1.16 0.278 0.275 -1.67 -0.760 1.00 4261. #> # ℹ 95 more rows #> # ℹ 1 more variable: ess_tail <dbl> fit$print() #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> lp__ -65.96 -65.63 1.45 1.26 -68.70 -64.28 1.00 1922 2690 -#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617 -#> beta[1] -0.67 -0.66 0.25 0.26 -1.09 -0.26 1.00 3806 3246 -#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.08 1.00 3937 2974 -#> beta[3] 0.68 0.67 0.27 0.26 0.25 1.13 1.00 3913 3133 -#> log_lik[1] -0.52 -0.51 0.10 0.10 -0.69 -0.36 1.00 4145 3089 -#> log_lik[2] -0.40 -0.39 0.15 0.14 -0.68 -0.20 1.00 4342 3107 -#> log_lik[3] -0.50 -0.47 0.22 0.21 -0.91 -0.21 1.00 4196 3253 -#> log_lik[4] -0.45 -0.43 0.15 0.15 -0.72 -0.23 1.00 3437 2969 -#> log_lik[5] -1.18 -1.16 0.28 0.28 -1.68 -0.75 1.00 4471 3204 +#> lp__ -65.96 -65.62 1.45 1.21 -68.75 -64.29 1.00 2232 2919 +#> alpha 0.38 0.38 0.21 0.22 0.03 0.73 1.00 4473 3190 +#> beta[1] -0.67 -0.66 0.25 0.25 -1.08 -0.26 1.00 4454 3056 +#> beta[2] -0.27 -0.27 0.23 0.23 -0.65 0.10 1.00 4166 2823 +#> beta[3] 0.68 0.67 0.26 0.26 0.25 1.12 1.00 3555 3140 +#> log_lik[1] -0.51 -0.51 0.10 0.10 -0.68 -0.37 1.00 4272 3239 +#> log_lik[2] -0.40 -0.38 0.15 0.14 -0.68 -0.20 1.00 4474 3106 +#> log_lik[3] -0.50 -0.46 0.22 0.20 -0.90 -0.20 1.00 4228 3323 +#> log_lik[4] -0.45 -0.43 0.15 0.15 -0.72 -0.24 1.00 3833 2800 +#> log_lik[5] -1.18 -1.16 0.28 0.28 -1.67 -0.76 1.00 4260 3060 #> #> # showing 10 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option) fit$print(max_rows = 2) # same as print(fit, max_rows = 2) #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> lp__ -65.96 -65.63 1.45 1.26 -68.70 -64.28 1.00 1922 2690 -#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617 +#> lp__ -65.96 -65.62 1.45 1.21 -68.75 -64.29 1.00 2232 2919 +#> alpha 0.38 0.38 0.21 0.22 0.03 0.73 1.00 4473 3190 #> #> # showing 2 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option) # include only certain variables fit$summary("beta") #> # A tibble: 3 × 10 -#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 beta[1] -0.669 -0.660 0.252 0.257 -1.09 -0.264 1.00 3807. 3246. -#> 2 beta[2] -0.279 -0.273 0.223 0.223 -0.649 0.0786 1.00 3937. 2974. -#> 3 beta[3] 0.677 0.669 0.267 0.257 0.248 1.13 1.00 3914. 3133. +#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 beta[1] -0.667 -0.665 0.248 0.254 -1.08 -0.261 1.00 4454. 3056. +#> 2 beta[2] -0.274 -0.271 0.233 0.232 -0.649 0.103 1.00 4166. 2824. +#> 3 beta[3] 0.680 0.672 0.264 0.258 0.254 1.12 1.00 3555. 3140. fit$print(c("alpha", "beta[2]")) #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617 -#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.08 1.00 3937 2974 +#> alpha 0.38 0.38 0.21 0.22 0.03 0.73 1.00 4473 3190 +#> beta[2] -0.27 -0.27 0.23 0.23 -0.65 0.10 1.00 4166 2823 # include all variables but only certain summaries fit$summary(NULL, c("mean", "sd")) @@ -217,15 +217,15 @@

    Examples

    #> variable mean sd #> <chr> <dbl> <dbl> #> 1 lp__ -66.0 1.45 -#> 2 alpha 0.378 0.221 -#> 3 beta[1] -0.669 0.252 -#> 4 beta[2] -0.279 0.223 -#> 5 beta[3] 0.677 0.267 -#> 6 log_lik[1] -0.516 0.0985 -#> 7 log_lik[2] -0.404 0.148 -#> 8 log_lik[3] -0.502 0.215 -#> 9 log_lik[4] -0.447 0.150 -#> 10 log_lik[5] -1.18 0.280 +#> 2 alpha 0.382 0.214 +#> 3 beta[1] -0.667 0.248 +#> 4 beta[2] -0.274 0.233 +#> 5 beta[3] 0.680 0.264 +#> 6 log_lik[1] -0.514 0.0973 +#> 7 log_lik[2] -0.403 0.147 +#> 8 log_lik[3] -0.497 0.217 +#> 9 log_lik[4] -0.450 0.152 +#> 10 log_lik[5] -1.18 0.278 #> # ℹ 95 more rows # can use functions created from formulas @@ -235,8 +235,8 @@

    Examples

    #> variable prob_gt_0 #> <chr> <dbl> #> 1 beta[1] 0.0015 -#> 2 beta[2] 0.102 -#> 3 beta[3] 0.993 +#> 2 beta[2] 0.118 +#> 3 beta[3] 0.994 # can combine user-specified functions with # the default summary functions @@ -248,10 +248,10 @@

    Examples

    #> # A tibble: 4 × 10 #> variable mean median sd mad q2.5 q97.5 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 alpha 0.378 0.373 0.221 0.218 -0.0434 0.812 1.00 4329. 2618. -#> 2 beta[1] -0.669 -0.660 0.252 0.257 -1.17 -0.191 1.00 3807. 3246. -#> 3 beta[2] -0.279 -0.273 0.223 0.223 -0.728 0.143 1.00 3937. 2974. -#> 4 beta[3] 0.677 0.669 0.267 0.257 0.164 1.22 1.00 3914. 3133. +#> 1 alpha 0.382 0.380 0.214 0.217 -0.0460 0.804 1.00 4473. 3191. +#> 2 beta[1] -0.667 -0.665 0.248 0.254 -1.16 -0.189 1.00 4454. 3056. +#> 3 beta[2] -0.274 -0.271 0.233 0.232 -0.727 0.182 1.00 4166. 2824. +#> 4 beta[3] 0.680 0.672 0.264 0.258 0.172 1.21 1.00 3555. 3140. # the functions need to calculate the appropriate # value for a matrix input diff --git a/docs/reference/fit-method-time.html b/docs/reference/fit-method-time.html index a966e10d1..4ec8378a2 100644 --- a/docs/reference/fit-method-time.html +++ b/docs/reference/fit-method-time.html @@ -21,7 +21,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -140,36 +140,36 @@

    Examples

    fit_mcmc <- cmdstanr_example("logistic", method = "sample") fit_mcmc$time() #> $total -#> [1] 1.020517 +#> [1] 0.938334 #> #> $chains #> chain_id warmup sampling total -#> 1 1 0.030 0.101 0.131 -#> 2 2 0.027 0.095 0.122 -#> 3 3 0.031 0.099 0.130 -#> 4 4 0.026 0.112 0.138 +#> 1 1 0.022 0.074 0.096 +#> 2 2 0.022 0.076 0.098 +#> 3 3 0.022 0.076 0.098 +#> 4 4 0.022 0.074 0.096 #> fit_vb <- cmdstanr_example("logistic", method = "variational") fit_vb$time() #> $total -#> [1] 0.1371388 +#> [1] 0.1466939 #> fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE) fit_mle$time() #> $total -#> [1] 0.1316919 +#> [1] 0.146842 #> # use fit_mle to draw samples from laplace approximation fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle) fit_laplace$time() # just time for drawing sample not for running optimize #> $total -#> [1] 0.131767 +#> [1] 0.1427319 #> fit_laplace$time()$total + fit_mle$time()$total # total time -#> [1] 0.263459 +#> [1] 0.2895739 # }     diff --git a/docs/reference/fit-method-unconstrain_draws.html b/docs/reference/fit-method-unconstrain_draws.html index 761b9f8e3..33a5af95d 100644 --- a/docs/reference/fit-method-unconstrain_draws.html +++ b/docs/reference/fit-method-unconstrain_draws.html @@ -22,7 +22,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/fit-method-unconstrain_variables.html b/docs/reference/fit-method-unconstrain_variables.html index 9ad0b93be..1d18cd8ee 100644 --- a/docs/reference/fit-method-unconstrain_variables.html +++ b/docs/reference/fit-method-unconstrain_variables.html @@ -18,7 +18,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/fit-method-variable_skeleton.html b/docs/reference/fit-method-variable_skeleton.html index 41318d097..defab3ed4 100644 --- a/docs/reference/fit-method-variable_skeleton.html +++ b/docs/reference/fit-method-variable_skeleton.html @@ -19,7 +19,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/index.html b/docs/reference/index.html index 34e0d0721..5537abbbe 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -288,7 +288,7 @@

    Fitted model objects and methods

    mle()

    -

    Extract (penalized) maximum likelihood estimate after optimization

    +

    Extract point estimate after optimization

    num_chains()

    diff --git a/docs/reference/install_cmdstan.html b/docs/reference/install_cmdstan.html index 8f4dbe2a3..0cdf27c32 100644 --- a/docs/reference/install_cmdstan.html +++ b/docs/reference/install_cmdstan.html @@ -16,7 +16,15 @@ should typically be followed by calling rebuild_cmdstan(). The check_cmdstan_toolchain() function attempts to check for the required C++ toolchain. It is called internally by install_cmdstan() but can also -be called directly by the user."> @@ -34,7 +42,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -138,7 +146,15 @@

    Install CmdStan or clean and rebuild an existing installation

    should typically be followed by calling rebuild_cmdstan().

    The check_cmdstan_toolchain() function attempts to check for the required C++ toolchain. It is called internally by install_cmdstan() but can also -be called directly by the user.

    +be called directly by the user. On Windows only, calling the function with +the fix = TRUE argument will attempt to install the necessary toolchain +components if they are not found. For Windows users with RTools and CmdStan +versions >= 2.35 no additional toolchain configuration is required.

    +

    NOTE: When installing CmdStan on Windows with RTools and CmdStan versions +prior to 2.35.0, the above additional toolchain configuration +is still required. To enable this configuration, set the environment variable +CMDSTANR_USE_MSYS_TOOLCHAIN to 'true' and call +check_cmdstan_toolchain(fix = TRUE).

    diff --git a/docs/reference/model-method-check_syntax.html b/docs/reference/model-method-check_syntax.html index 36f1d1d85..39684030b 100644 --- a/docs/reference/model-method-check_syntax.html +++ b/docs/reference/model-method-check_syntax.html @@ -19,7 +19,7 @@ cmdstanr - 0.8.1 + 0.9.0
    @@ -202,7 +202,7 @@

    Examples

    # pedantic mode will warn that lambda should be constrained to be positive # and that lambda has no prior distribution mod$check_syntax(pedantic = TRUE) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A +#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/model_febb1e69c7387a0e64cf13583e078104.stan', line 11, column 14: A #> poisson distribution is given parameter lambda as a rate parameter #> (argument 1), but lambda was not constrained to be strictly positive. #> Warning: The parameter lambda has no priors. This means either no prior is diff --git a/docs/reference/model-method-compile.html b/docs/reference/model-method-compile.html index 26d474489..6e749d1cd 100644 --- a/docs/reference/model-method-compile.html +++ b/docs/reference/model-method-compile.html @@ -31,7 +31,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -171,7 +171,7 @@

    Arguments

    pedantic

    (logical) Should pedantic mode be turned on? The default is FALSE. Pedantic mode attempts to warn you about potential issues in your -Stan program beyond syntax errors. For details see the Pedantic mode chapter in +Stan program beyond syntax errors. For details see the Pedantic mode section in the Stan Reference Manual. Note: to do a pedantic check for a model without compiling it or for a model that is already compiled the $check_syntax() method can be used instead.

    @@ -286,12 +286,12 @@

    Examples

    mod <- cmdstan_model(file, compile = FALSE) mod$compile() mod$exe_file() -#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli" +#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli" # turn on threading support (for using functions that support within-chain parallelization) mod$compile(force_recompile = TRUE, cpp_options = list(stan_threads = TRUE)) mod$exe_file() -#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli" +#> [1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli" # turn on pedantic mode (new in Stan v2.24) file_pedantic <- write_stan_file(" @@ -303,7 +303,7 @@

    Examples

    } ") mod <- cmdstan_model(file_pedantic, pedantic = TRUE) -#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model-59c030d64420.stan', line 6, column 2: Parameter +#> Warning in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/model-b9aa1e29138e.stan', line 6, column 2: Parameter #> sigma is given a exponential distribution, which has strictly positive #> support, but sigma was not constrained to be strictly positive. diff --git a/docs/reference/model-method-diagnose.html b/docs/reference/model-method-diagnose.html index 1ad592eff..41e361f40 100644 --- a/docs/reference/model-method-diagnose.html +++ b/docs/reference/model-method-diagnose.html @@ -21,7 +21,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -270,11 +270,11 @@

    Examples

    # retrieve the gradients test$gradients() -#> param_idx value model finite_diff error -#> 1 0 0.591940 0.00146498 0.00146493 4.91040e-08 -#> 2 1 1.390710 -30.34660000 -30.34660000 -4.69705e-09 -#> 3 2 -0.987687 11.19930000 11.19930000 1.90498e-08 -#> 4 3 -1.426340 28.46830000 28.46830000 1.54688e-08 +#> param_idx value model finite_diff error +#> 1 0 -1.760420 33.15260 33.15260 2.88651e-08 +#> 2 1 -1.814920 5.15400 5.15400 6.47713e-09 +#> 3 2 0.718975 -16.58770 -16.58770 2.24132e-08 +#> 4 3 1.697010 -5.70682 -5.70682 5.89145e-10 # } diff --git a/docs/reference/model-method-expose_functions.html b/docs/reference/model-method-expose_functions.html index 6032f96f0..f401d289c 100644 --- a/docs/reference/model-method-expose_functions.html +++ b/docs/reference/model-method-expose_functions.html @@ -25,7 +25,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/model-method-format.html b/docs/reference/model-method-format.html index 92f6c52d9..a3546c03a 100644 --- a/docs/reference/model-method-format.html +++ b/docs/reference/model-method-format.html @@ -19,7 +19,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -187,72 +187,6 @@

    See also

    Examples

    # \dontrun{
 
-# Example of fixing old syntax
-# real x[2] --> array[2] real x;
-file <- write_stan_file("
-parameters {
-  real x[2];
-}
-model {
-  x ~ std_normal();
-}
-")
-
-# set compile=FALSE then call format to fix old syntax
-mod <- cmdstan_model(file, compile = FALSE)
-mod$format(canonicalize = list("deprecations"))
-#> Syntax error in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model_1fc88c86300d78667dd3e476a636c279.stan', line 3, column 9 to column 10, parsing error:
-#>    -------------------------------------------------
-#>      1:  
-#>      2:  parameters {
-#>      3:    real x[2];
-#>                   ^
-#>      4:  }
-#>      5:  model {
-#>    -------------------------------------------------
-#> 
-#> ";" expected after variable declaration.
-#> It looks like you are trying to use the old array syntax.
-#> Please use the new syntax:
-#> array[2] real x;
-#> Error: Syntax error found! See the message above for more information.
-
-# overwrite the original file instead of just printing it
-mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
-#> Syntax error in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model_1fc88c86300d78667dd3e476a636c279.stan', line 3, column 9 to column 10, parsing error:
-#>    -------------------------------------------------
-#>      1:  
-#>      2:  parameters {
-#>      3:    real x[2];
-#>                   ^
-#>      4:  }
-#>      5:  model {
-#>    -------------------------------------------------
-#> 
-#> ";" expected after variable declaration.
-#> It looks like you are trying to use the old array syntax.
-#> Please use the new syntax:
-#> array[2] real x;
-#> Error: Syntax error found! See the message above for more information.
-mod$compile()
-#> Syntax error in '/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model-59c028cea1fd.stan', line 3, column 9 to column 10, parsing error:
-#>    -------------------------------------------------
-#>      1:  
-#>      2:  parameters {
-#>      3:    real x[2];
-#>                   ^
-#>      4:  }
-#>      5:  model {
-#>    -------------------------------------------------
-#> 
-#> ";" expected after variable declaration.
-#> It looks like you are trying to use the old array syntax.
-#> Please use the new syntax:
-#> array[2] real x;
-#> make: *** [/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model-59c028cea1fd.hpp] Error 1
-#> Error: An error occured during compilation! See the message above for more information.
-
-
 # Example of removing unnecessary whitespace
 file <- write_stan_file("
 data {
@@ -267,8 +201,10 @@ 
    Examples
    
  poisson_lpmf(y | lambda);
 }
 ")
+
+# set compile=FALSE then call format to fix old syntax
 mod <- cmdstan_model(file, compile = FALSE)
-mod$format(canonicalize = TRUE)
+mod$format(canonicalize = list("deprecations"))
 #> data {
 #>   int N;
 #>   array[N] int y;
@@ -281,6 +217,11 @@ 
    Examples
    
 #> }
 #> 
 #> 
+
+# overwrite the original file instead of just printing it
+mod$format(canonicalize = list("deprecations"), overwrite_file = TRUE)
+#> Old version of the model stored to /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/model_39022cccc3fe5384fab5a52b791fead6.stan.bak-20250331084850.
+mod$compile()
 # }
    diff --git a/docs/reference/model-method-generate-quantities.html b/docs/reference/model-method-generate-quantities.html index a7c397f90..d7278dd5e 100644 --- a/docs/reference/model-method-generate-quantities.html +++ b/docs/reference/model-method-generate-quantities.html @@ -19,7 +19,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/model-method-laplace-1.png b/docs/reference/model-method-laplace-1.png index ddc254975..823e448ec 100644 Binary files a/docs/reference/model-method-laplace-1.png and b/docs/reference/model-method-laplace-1.png differ diff --git a/docs/reference/model-method-laplace.html b/docs/reference/model-method-laplace.html index c510e97af..39a307131 100644 --- a/docs/reference/model-method-laplace.html +++ b/docs/reference/model-method-laplace.html @@ -28,7 +28,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -384,12 +384,12 @@

    Examples

    stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1)) fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE) -#> Initial log joint probability = -7.3571 +#> Initial log joint probability = -7.39766 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 5 -6.74802 0.000511122 9.02231e-07 1 1 8 +#> 5 -6.74802 8.90809e-05 3.13416e-07 1 1 8 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance -#> Finished in 0.3 seconds. +#> Finished in 0.2 seconds. fit_laplace <- mod$laplace(data = stan_data, mode = fit_mode) #> Calculating Hessian #> Calculating inverse of Cholesky factor @@ -407,17 +407,17 @@

    Examples

    #> Finished in 0.2 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 -#> variable mean median sd mad q5 q95 -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.24 -6.97 0.734 0.309 -8.50 -6.75 -#> 2 lp_approx__ -0.491 -0.223 0.698 0.309 -1.80 -0.00200 -#> 3 theta 0.271 0.250 0.123 0.123 0.103 0.487 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.26 -6.98 0.724 0.319 -8.80 -6.75 +#> 2 lp_approx__ -0.533 -0.245 0.740 0.330 -2.10 -0.00300 +#> 3 theta 0.262 0.240 0.125 0.120 0.0941 0.503 # if mode isn't specified optimize is run internally first fit_laplace <- mod$laplace(data = stan_data) -#> Initial log joint probability = -11.0359 +#> Initial log joint probability = -7.50846 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 5 -6.74802 0.000945646 1.40836e-05 1 1 8 +#> 5 -6.74802 0.000114292 4.68497e-07 1 1 8 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds. @@ -434,14 +434,14 @@

    Examples

    #> iteration: 700 #> iteration: 800 #> iteration: 900 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.26 -6.97 0.744 0.306 -8.73 -6.75 -#> 2 lp_approx__ -0.516 -0.226 0.742 0.306 -1.93 -0.00178 -#> 3 theta 0.270 0.250 0.126 0.121 0.103 0.513 +#> 1 lp__ -7.23 -6.96 0.773 0.291 -8.49 -6.75 +#> 2 lp_approx__ -0.485 -0.219 0.744 0.303 -1.81 -0.00163 +#> 3 theta 0.265 0.250 0.121 0.121 0.101 0.493 # plot approximate posterior bayesplot::mcmc_hist(fit_laplace$draws("theta")) diff --git a/docs/reference/model-method-optimize-1.png b/docs/reference/model-method-optimize-1.png index aa185638c..35206a7af 100644 Binary files a/docs/reference/model-method-optimize-1.png and b/docs/reference/model-method-optimize-1.png differ diff --git a/docs/reference/model-method-optimize-2.png b/docs/reference/model-method-optimize-2.png index 4cf81fa80..b078570c6 100644 Binary files a/docs/reference/model-method-optimize-2.png and b/docs/reference/model-method-optimize-2.png differ diff --git a/docs/reference/model-method-optimize-3.png b/docs/reference/model-method-optimize-3.png index c67b37ed1..35367ed42 100644 Binary files a/docs/reference/model-method-optimize-3.png and b/docs/reference/model-method-optimize-3.png differ diff --git a/docs/reference/model-method-optimize.html b/docs/reference/model-method-optimize.html index ff5a7c21a..ff5bdc696 100644 --- a/docs/reference/model-method-optimize.html +++ b/docs/reference/model-method-optimize.html @@ -1,7 +1,13 @@ Run Stan's optimization algorithms — model-method-optimize • cmdstanr cmdstanr - 0.8.1 + 0.9.0 @@ -114,8 +120,14 @@

    Run Stan's optimization algorithms

    The $optimize() method of a CmdStanModel object runs -Stan's optimizer to obtain a (penalized) maximum likelihood estimate or a -maximum a posteriori estimate (if jacobian=TRUE). See the +Stan's optimizer to obtain a (penalized) maximum likelihood estimate (MLE) +or a maximum a posteriori estimate (MAP), depending on the value of the +jacobian argument. For models with constrained parameters, when the +Jacobian adjustment is not applied, the point estimate corresponds to a +penalized MLE, and when the Jacobian adjustment is applied the point +estimate corresponds to the MAP (posterior mode) of the model we would fit +if we were instead doing MCMC sampling. The Jacobian adjustment has no +affect if the model has only unconstrained parameters. See the CmdStan User's Guide for more details.

    Any argument left as NULL will default to the default value used by the @@ -410,7 +422,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -512,8 +524,8 @@

    Examples

    #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.30 -7.00 0.811 0.344 -8.83 -6.75 1.00 702. 776. -#> 2 theta 0.254 0.238 0.125 0.124 0.0807 0.483 1.00 634. 580. +#> 1 lp__ -7.35 -7.01 0.882 0.353 -9.14 -6.75 1.00 724. 896. +#> 2 theta 0.254 0.239 0.129 0.126 0.0737 0.488 1.00 532. 657. # Check sampling diagnostics fit_mcmc$diagnostic_summary() @@ -524,7 +536,7 @@

    Examples

    #> [1] 0 0 #> #> $ebfmi -#> [1] 1.1148699 0.8012192 +#> [1] 1.1148479 0.7568734 #> # Get posterior draws @@ -534,22 +546,22 @@

    Examples

    #> , , variable = lp__ #> #> chain -#> iteration 1 2 -#> 1 -7.0 -6.8 -#> 2 -7.9 -6.9 -#> 3 -7.4 -6.8 -#> 4 -6.7 -6.8 -#> 5 -6.9 -10.2 +#> iteration 1 2 +#> 1 -7.0 -8.1 +#> 2 -7.9 -7.9 +#> 3 -7.4 -7.0 +#> 4 -6.7 -6.8 +#> 5 -6.9 -6.8 #> #> , , variable = theta #> #> chain -#> iteration 1 2 -#> 1 0.17 0.23 -#> 2 0.46 0.18 -#> 3 0.41 0.28 -#> 4 0.25 0.23 -#> 5 0.18 0.62 +#> iteration 1 2 +#> 1 0.17 0.088 +#> 2 0.46 0.097 +#> 3 0.41 0.167 +#> 4 0.25 0.292 +#> 5 0.18 0.238 #> #> # ... with 995 more iterations @@ -575,11 +587,6 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# For models fit using MCMC, if you like working with RStan's stanfit objects -# then you can create one with rstan::read_stan_csv() -# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) - - # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm) # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") @@ -600,9 +607,9 @@

    Examples

    # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation # to the posterior fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) -#> Initial log joint probability = -11.9711 +#> Initial log joint probability = -12.6782 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 5 -6.74802 0.00123686 2.20415e-05 1 1 8 +#> 5 -6.74802 0.00154762 3.33598e-05 1 1 8 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.2 seconds. @@ -630,14 +637,14 @@

    Examples

    #> iteration: 1700 #> iteration: 1800 #> iteration: 1900 -#> Finished in 0.2 seconds. +#> Finished in 0.1 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 -#> variable mean median sd mad q5 q95 -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.23 -6.96 0.697 0.297 -8.63 -6.75 -#> 2 lp_approx__ -0.491 -0.213 0.686 0.296 -1.89 -0.00171 -#> 3 theta 0.268 0.249 0.123 0.118 0.102 0.507 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.23 -6.96 0.673 0.289 -8.53 -6.75 +#> 2 lp_approx__ -0.484 -0.215 0.665 0.294 -1.83 -0.00153 +#> 3 theta 0.270 0.253 0.121 0.119 0.0987 0.501 # Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) @@ -646,8 +653,8 @@

    Examples

    #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 1.2e-05 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds. +#> Gradient evaluation took 1.1e-05 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -663,7 +670,7 @@

    Examples

    #> 300 -6.186 0.339 0.010 MEDIAN ELBO CONVERGED #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 @@ -694,7 +701,7 @@

    Examples

    #> 5 -6.748e+00 2.145e-04 1.301e-06 1.000e+00 1.000e+00 126 -6.197e+00 -6.197e+00 #> Path [4] :Best Iter: [5] ELBO (-6.197118) evaluations: (126) #> Total log probability function evaluations:4379 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_pf$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 @@ -711,49 +718,48 @@

    Examples

    fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40, history_size=50, max_lbfgs_iters=100) #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. -#> Path [1] :Initial log joint density = -14.241761 +#> Path [1] :Initial log joint density = -6.755726 #> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 2.000e-03 5.597e-05 1.000e+00 1.000e+00 126 -6.236e+00 -6.236e+00 -#> Path [1] :Best Iter: [5] ELBO (-6.235525) evaluations: (126) -#> Path [2] :Initial log joint density = -13.402374 +#> 3 -6.748e+00 9.605e-04 1.028e-05 9.884e-01 9.884e-01 76 -6.240e+00 -6.240e+00 +#> Path [1] :Best Iter: [2] ELBO (-6.221702) evaluations: (76) +#> Path [2] :Initial log joint density = -9.051867 #> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.810e-03 4.511e-05 1.000e+00 1.000e+00 126 -6.208e+00 -6.208e+00 -#> Path [2] :Best Iter: [2] ELBO (-6.203915) evaluations: (126) -#> Path [3] :Initial log joint density = -12.050800 +#> 5 -6.748e+00 3.457e-04 2.924e-06 1.000e+00 1.000e+00 126 -6.190e+00 -6.190e+00 +#> Path [2] :Best Iter: [5] ELBO (-6.189740) evaluations: (126) +#> Path [3] :Initial log joint density = -6.775220 #> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.274e-03 2.325e-05 1.000e+00 1.000e+00 126 -6.196e+00 -6.196e+00 -#> Path [3] :Best Iter: [3] ELBO (-6.168581) evaluations: (126) -#> Path [4] :Initial log joint density = -11.907691 +#> 3 -6.748e+00 2.337e-03 3.532e-06 9.682e-01 9.682e-01 76 -6.298e+00 -6.298e+00 +#> Path [3] :Best Iter: [3] ELBO (-6.298399) evaluations: (76) +#> Path [4] :Initial log joint density = -18.179237 #> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.208e-03 2.110e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00 -#> Path [4] :Best Iter: [3] ELBO (-6.184266) evaluations: (126) -#> Path [5] :Initial log joint density = -17.062583 +#> 5 -6.748e+00 7.662e-04 1.607e-05 1.000e+00 1.000e+00 126 -6.271e+00 -6.271e+00 +#> Path [4] :Best Iter: [4] ELBO (-6.175731) evaluations: (126) +#> Path [5] :Initial log joint density = -17.845239 #> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.424e-03 3.915e-05 1.000e+00 1.000e+00 126 -6.229e+00 -6.229e+00 -#> Path [5] :Best Iter: [4] ELBO (-6.180989) evaluations: (126) -#> Path [6] :Initial log joint density = -7.950926 +#> 5 -6.748e+00 9.725e-04 2.274e-05 1.000e+00 1.000e+00 126 -6.275e+00 -6.275e+00 +#> Path [5] :Best Iter: [4] ELBO (-6.207498) evaluations: (126) +#> Path [6] :Initial log joint density = -11.863276 #> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 2.149e-04 1.305e-06 1.000e+00 1.000e+00 126 -6.210e+00 -6.210e+00 -#> Path [6] :Best Iter: [4] ELBO (-6.193547) evaluations: (126) -#> Path [7] :Initial log joint density = -13.337353 +#> 5 -6.748e+00 1.187e-03 2.045e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [6] :Best Iter: [2] ELBO (-6.141104) evaluations: (126) +#> Path [7] :Initial log joint density = -6.807183 #> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.789e-03 4.411e-05 1.000e+00 1.000e+00 126 -6.275e+00 -6.275e+00 -#> Path [7] :Best Iter: [4] ELBO (-6.264759) evaluations: (126) -#> Path [8] :Initial log joint density = -6.856154 +#> 4 -6.748e+00 3.391e-04 1.605e-06 1.000e+00 1.000e+00 101 -6.264e+00 -6.264e+00 +#> Path [7] :Best Iter: [2] ELBO (-6.199985) evaluations: (101) +#> Path [8] :Initial log joint density = -6.874334 #> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 7.559e-04 6.106e-06 1.000e+00 1.000e+00 101 -6.237e+00 -6.237e+00 -#> Path [8] :Best Iter: [2] ELBO (-6.233924) evaluations: (101) -#> Path [9] :Initial log joint density = -19.158821 +#> 4 -6.748e+00 1.370e-04 2.042e-05 9.398e-01 9.398e-01 101 -6.203e+00 -6.203e+00 +#> Path [8] :Best Iter: [4] ELBO (-6.203174) evaluations: (101) +#> Path [9] :Initial log joint density = -16.813276 #> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 2.167e-04 2.482e-06 1.000e+00 1.000e+00 126 -6.251e+00 -6.251e+00 -#> Path [9] :Best Iter: [3] ELBO (-6.196829) evaluations: (126) -#> Path [10] :Initial log joint density = -6.894545 +#> 5 -6.748e+00 1.549e-03 4.398e-05 1.000e+00 1.000e+00 126 -6.174e+00 -6.174e+00 +#> Path [9] :Best Iter: [5] ELBO (-6.174126) evaluations: (126) +#> Path [10] :Initial log joint density = -11.429555 #> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 1.811e-04 2.981e-05 9.341e-01 9.341e-01 101 -6.227e+00 -6.227e+00 -#> Path [10] :Best Iter: [2] ELBO (-6.144040) evaluations: (101) -#> Total log probability function evaluations:1360 -#> Pareto k value (0.73) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. -#> Finished in 0.1 seconds. +#> 5 -6.748e+00 1.045e-03 1.644e-05 1.000e+00 1.000e+00 126 -6.293e+00 -6.293e+00 +#> Path [10] :Best Iter: [3] ELBO (-6.173918) evaluations: (126) +#> Total log probability function evaluations:1260 +#> Finished in 0.2 seconds. # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( @@ -789,7 +795,7 @@

    Examples

    #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. -#> Total execution time: 0.3 seconds. +#> Total execution time: 0.4 seconds. #> fit_mcmc_w_init_fun_2$init() #> [[1]] diff --git a/docs/reference/model-method-pathfinder-1.png b/docs/reference/model-method-pathfinder-1.png index aa185638c..35206a7af 100644 Binary files a/docs/reference/model-method-pathfinder-1.png and b/docs/reference/model-method-pathfinder-1.png differ diff --git a/docs/reference/model-method-pathfinder-2.png b/docs/reference/model-method-pathfinder-2.png index 4cf81fa80..b078570c6 100644 Binary files a/docs/reference/model-method-pathfinder-2.png and b/docs/reference/model-method-pathfinder-2.png differ diff --git a/docs/reference/model-method-pathfinder-3.png b/docs/reference/model-method-pathfinder-3.png index c67b37ed1..35367ed42 100644 Binary files a/docs/reference/model-method-pathfinder-3.png and b/docs/reference/model-method-pathfinder-3.png differ diff --git a/docs/reference/model-method-pathfinder.html b/docs/reference/model-method-pathfinder.html index fabd8cf2f..c338a2b62 100644 --- a/docs/reference/model-method-pathfinder.html +++ b/docs/reference/model-method-pathfinder.html @@ -32,7 +32,7 @@ cmdstanr - 0.8.1 + 0.9.0
    @@ -451,7 +451,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -553,8 +553,8 @@

    Examples

    #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.30 -7.00 0.811 0.344 -8.83 -6.75 1.00 702. 776. -#> 2 theta 0.254 0.238 0.125 0.124 0.0807 0.483 1.00 634. 580. +#> 1 lp__ -7.35 -7.01 0.882 0.353 -9.14 -6.75 1.00 724. 896. +#> 2 theta 0.254 0.239 0.129 0.126 0.0737 0.488 1.00 532. 657. # Check sampling diagnostics fit_mcmc$diagnostic_summary() @@ -565,7 +565,7 @@

    Examples

    #> [1] 0 0 #> #> $ebfmi -#> [1] 1.1148699 0.8012192 +#> [1] 1.1148479 0.7568734 #> # Get posterior draws @@ -575,22 +575,22 @@

    Examples

    #> , , variable = lp__ #> #> chain -#> iteration 1 2 -#> 1 -7.0 -6.8 -#> 2 -7.9 -6.9 -#> 3 -7.4 -6.8 -#> 4 -6.7 -6.8 -#> 5 -6.9 -10.2 +#> iteration 1 2 +#> 1 -7.0 -8.1 +#> 2 -7.9 -7.9 +#> 3 -7.4 -7.0 +#> 4 -6.7 -6.8 +#> 5 -6.9 -6.8 #> #> , , variable = theta #> #> chain -#> iteration 1 2 -#> 1 0.17 0.23 -#> 2 0.46 0.18 -#> 3 0.41 0.28 -#> 4 0.25 0.23 -#> 5 0.18 0.62 +#> iteration 1 2 +#> 1 0.17 0.088 +#> 2 0.46 0.097 +#> 3 0.41 0.167 +#> 4 0.25 0.292 +#> 5 0.18 0.238 #> #> # ... with 995 more iterations @@ -616,11 +616,6 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# For models fit using MCMC, if you like working with RStan's stanfit objects -# then you can create one with rstan::read_stan_csv() -# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) - - # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm) # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") @@ -630,7 +625,7 @@

    Examples

    #> 6 -5.00402 0.000246518 8.73164e-07 1 1 9 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate @@ -641,12 +636,12 @@

    Examples

    # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation # to the posterior fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) -#> Initial log joint probability = -8.03582 +#> Initial log joint probability = -11.6636 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 5 -6.74802 0.000232501 1.48433e-06 1 1 8 +#> 5 -6.74802 0.00109631 1.77159e-05 1 1 8 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) #> Calculating Hessian #> Calculating inverse of Cholesky factor @@ -671,14 +666,14 @@

    Examples

    #> iteration: 1700 #> iteration: 1800 #> iteration: 1900 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 -#> variable mean median sd mad q5 q95 -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.24 -6.98 0.696 0.318 -8.64 -6.75 -#> 2 lp_approx__ -0.504 -0.233 0.703 0.320 -1.97 -0.00198 -#> 3 theta 0.266 0.248 0.123 0.122 0.100 0.498 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.26 -6.97 0.807 0.300 -8.67 -6.75 +#> 2 lp_approx__ -0.514 -0.222 0.798 0.305 -1.93 -0.00172 +#> 3 theta 0.270 0.254 0.125 0.122 0.0996 0.507 # Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) @@ -687,8 +682,8 @@

    Examples

    #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 1.2e-05 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds. +#> Gradient evaluation took 1.1e-05 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -704,7 +699,7 @@

    Examples

    #> 300 -6.186 0.339 0.010 MEDIAN ELBO CONVERGED #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 @@ -752,48 +747,48 @@

    Examples

    fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40, history_size=50, max_lbfgs_iters=100) #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. -#> Path [1] :Initial log joint density = -6.748022 +#> Path [1] :Initial log joint density = -8.866534 #> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 2 -6.748e+00 3.660e-04 9.724e-08 1.000e+00 1.000e+00 51 -6.176e+00 -6.176e+00 -#> Path [1] :Best Iter: [2] ELBO (-6.176168) evaluations: (51) -#> Path [2] :Initial log joint density = -6.810066 +#> 5 -6.748e+00 3.406e-04 2.829e-06 1.000e+00 1.000e+00 126 -6.250e+00 -6.250e+00 +#> Path [1] :Best Iter: [3] ELBO (-6.238275) evaluations: (126) +#> Path [2] :Initial log joint density = -12.781290 #> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 3.617e-04 1.786e-06 1.000e+00 1.000e+00 101 -6.202e+00 -6.202e+00 -#> Path [2] :Best Iter: [4] ELBO (-6.201718) evaluations: (101) -#> Path [3] :Initial log joint density = -7.451465 +#> 5 -6.748e+00 1.589e-03 3.507e-05 1.000e+00 1.000e+00 126 -6.162e+00 -6.162e+00 +#> Path [2] :Best Iter: [4] ELBO (-6.140443) evaluations: (126) +#> Path [3] :Initial log joint density = -7.160474 #> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.012e-04 3.850e-07 1.000e+00 1.000e+00 126 -6.225e+00 -6.225e+00 -#> Path [3] :Best Iter: [4] ELBO (-6.186839) evaluations: (126) -#> Path [4] :Initial log joint density = -18.081755 +#> 5 -6.748e+00 1.830e-04 1.466e-07 1.000e+00 1.000e+00 126 -6.227e+00 -6.227e+00 +#> Path [3] :Best Iter: [2] ELBO (-6.155847) evaluations: (126) +#> Path [4] :Initial log joint density = -15.099639 #> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 8.265e-04 1.795e-05 1.000e+00 1.000e+00 126 -6.189e+00 -6.189e+00 -#> Path [4] :Best Iter: [5] ELBO (-6.188909) evaluations: (126) -#> Path [5] :Initial log joint density = -7.075204 +#> 5 -6.748e+00 2.032e-03 6.062e-05 1.000e+00 1.000e+00 126 -6.233e+00 -6.233e+00 +#> Path [4] :Best Iter: [3] ELBO (-6.206690) evaluations: (126) +#> Path [5] :Initial log joint density = -8.969584 #> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 2.957e-03 5.925e-05 1.000e+00 1.000e+00 101 -6.268e+00 -6.268e+00 -#> Path [5] :Best Iter: [3] ELBO (-6.207400) evaluations: (101) -#> Path [6] :Initial log joint density = -6.950110 +#> 5 -6.748e+00 3.443e-04 2.894e-06 1.000e+00 1.000e+00 126 -6.229e+00 -6.229e+00 +#> Path [5] :Best Iter: [4] ELBO (-6.164928) evaluations: (126) +#> Path [6] :Initial log joint density = -7.603579 #> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 1.668e-03 2.284e-05 1.000e+00 1.000e+00 101 -6.226e+00 -6.226e+00 -#> Path [6] :Best Iter: [3] ELBO (-6.178796) evaluations: (101) -#> Path [7] :Initial log joint density = -10.742807 +#> 5 -6.748e+00 1.364e-04 6.236e-07 1.000e+00 1.000e+00 126 -6.247e+00 -6.247e+00 +#> Path [6] :Best Iter: [4] ELBO (-6.232661) evaluations: (126) +#> Path [7] :Initial log joint density = -6.767406 #> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 8.636e-04 1.223e-05 1.000e+00 1.000e+00 126 -6.253e+00 -6.253e+00 -#> Path [7] :Best Iter: [2] ELBO (-6.147651) evaluations: (126) -#> Path [8] :Initial log joint density = -8.722796 +#> 3 -6.748e+00 1.821e-03 5.050e-06 9.750e-01 9.750e-01 76 -6.198e+00 -6.198e+00 +#> Path [7] :Best Iter: [3] ELBO (-6.197967) evaluations: (76) +#> Path [8] :Initial log joint density = -7.131671 #> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 3.316e-04 2.692e-06 1.000e+00 1.000e+00 126 -6.185e+00 -6.185e+00 -#> Path [8] :Best Iter: [5] ELBO (-6.185211) evaluations: (126) -#> Path [9] :Initial log joint density = -6.756313 +#> 5 -6.748e+00 1.518e-04 1.054e-07 1.000e+00 1.000e+00 126 -6.299e+00 -6.299e+00 +#> Path [8] :Best Iter: [2] ELBO (-6.177966) evaluations: (126) +#> Path [9] :Initial log joint density = -12.644619 #> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 3 -6.748e+00 1.308e-03 4.802e-05 1.000e+00 1.000e+00 76 -6.202e+00 -6.202e+00 -#> Path [9] :Best Iter: [3] ELBO (-6.201510) evaluations: (76) -#> Path [10] :Initial log joint density = -7.046682 +#> 5 -6.748e+00 1.534e-03 3.280e-05 1.000e+00 1.000e+00 126 -6.242e+00 -6.242e+00 +#> Path [9] :Best Iter: [3] ELBO (-6.212996) evaluations: (126) +#> Path [10] :Initial log joint density = -9.270214 #> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 2.661e-03 4.972e-05 1.000e+00 1.000e+00 101 -6.199e+00 -6.199e+00 -#> Path [10] :Best Iter: [2] ELBO (-6.191324) evaluations: (101) -#> Total log probability function evaluations:1185 -#> Finished in 0.1 seconds. +#> 5 -6.748e+00 3.999e-04 3.679e-06 1.000e+00 1.000e+00 126 -6.217e+00 -6.217e+00 +#> Path [10] :Best Iter: [4] ELBO (-6.202150) evaluations: (126) +#> Total log probability function evaluations:1360 +#> Finished in 0.2 seconds. # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( diff --git a/docs/reference/model-method-sample-1.png b/docs/reference/model-method-sample-1.png index aa185638c..35206a7af 100644 Binary files a/docs/reference/model-method-sample-1.png and b/docs/reference/model-method-sample-1.png differ diff --git a/docs/reference/model-method-sample-2.png b/docs/reference/model-method-sample-2.png index 4cf81fa80..b078570c6 100644 Binary files a/docs/reference/model-method-sample-2.png and b/docs/reference/model-method-sample-2.png differ diff --git a/docs/reference/model-method-sample-3.png b/docs/reference/model-method-sample-3.png index c67b37ed1..35367ed42 100644 Binary files a/docs/reference/model-method-sample-3.png and b/docs/reference/model-method-sample-3.png differ diff --git a/docs/reference/model-method-sample.html b/docs/reference/model-method-sample.html index a0b5b1f3c..db271e25b 100644 --- a/docs/reference/model-method-sample.html +++ b/docs/reference/model-method-sample.html @@ -24,7 +24,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -526,7 +526,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -628,8 +628,8 @@

    Examples

    #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.30 -7.00 0.811 0.344 -8.83 -6.75 1.00 702. 776. -#> 2 theta 0.254 0.238 0.125 0.124 0.0807 0.483 1.00 634. 580. +#> 1 lp__ -7.35 -7.01 0.882 0.353 -9.14 -6.75 1.00 724. 896. +#> 2 theta 0.254 0.239 0.129 0.126 0.0737 0.488 1.00 532. 657. # Check sampling diagnostics fit_mcmc$diagnostic_summary() @@ -640,7 +640,7 @@

    Examples

    #> [1] 0 0 #> #> $ebfmi -#> [1] 1.1148699 0.8012192 +#> [1] 1.1148479 0.7568734 #> # Get posterior draws @@ -650,22 +650,22 @@

    Examples

    #> , , variable = lp__ #> #> chain -#> iteration 1 2 -#> 1 -7.0 -6.8 -#> 2 -7.9 -6.9 -#> 3 -7.4 -6.8 -#> 4 -6.7 -6.8 -#> 5 -6.9 -10.2 +#> iteration 1 2 +#> 1 -7.0 -8.1 +#> 2 -7.9 -7.9 +#> 3 -7.4 -7.0 +#> 4 -6.7 -6.8 +#> 5 -6.9 -6.8 #> #> , , variable = theta #> #> chain -#> iteration 1 2 -#> 1 0.17 0.23 -#> 2 0.46 0.18 -#> 3 0.41 0.28 -#> 4 0.25 0.23 -#> 5 0.18 0.62 +#> iteration 1 2 +#> 1 0.17 0.088 +#> 2 0.46 0.097 +#> 3 0.41 0.167 +#> 4 0.25 0.292 +#> 5 0.18 0.238 #> #> # ... with 995 more iterations @@ -691,11 +691,6 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# For models fit using MCMC, if you like working with RStan's stanfit objects -# then you can create one with rstan::read_stan_csv() -# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) - - # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm) # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") @@ -716,9 +711,9 @@

    Examples

    # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation # to the posterior fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) -#> Initial log joint probability = -6.90422 +#> Initial log joint probability = -6.989 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 4 -6.74802 0.00121095 1.33942e-05 1 1 7 +#> 4 -6.74802 0.000455385 0.000104592 0.9108 0.9108 7 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.2 seconds. @@ -751,9 +746,9 @@

    Examples

    #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.23 -6.97 0.693 0.308 -8.63 -6.75 -#> 2 lp_approx__ -0.493 -0.228 0.700 0.315 -1.89 -0.00185 -#> 3 theta 0.265 0.245 0.122 0.119 0.102 0.495 +#> 1 lp__ -7.23 -6.97 0.700 0.304 -8.62 -6.75 +#> 2 lp_approx__ -0.482 -0.222 0.697 0.301 -1.89 -0.00177 +#> 3 theta 0.273 0.257 0.121 0.123 0.104 0.502 # Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) @@ -779,7 +774,7 @@

    Examples

    #> 300 -6.186 0.339 0.010 MEDIAN ELBO CONVERGED #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_vb$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 @@ -827,48 +822,47 @@

    Examples

    fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40, history_size=50, max_lbfgs_iters=100) #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. -#> Path [1] :Initial log joint density = -6.999713 +#> Path [1] :Initial log joint density = -14.842013 #> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 4.935e-04 1.167e-04 9.085e-01 9.085e-01 101 -6.235e+00 -6.235e+00 -#> Path [1] :Best Iter: [2] ELBO (-6.220514) evaluations: (101) -#> Path [2] :Initial log joint density = -6.963421 +#> 5 -6.748e+00 2.041e-03 6.008e-05 1.000e+00 1.000e+00 126 -6.245e+00 -6.245e+00 +#> Path [1] :Best Iter: [4] ELBO (-6.197550) evaluations: (126) +#> Path [2] :Initial log joint density = -13.719046 #> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 3.701e-04 7.882e-05 9.167e-01 9.167e-01 101 -6.331e+00 -6.331e+00 -#> Path [2] :Best Iter: [2] ELBO (-6.159890) evaluations: (101) -#> Path [3] :Initial log joint density = -6.794406 +#> 5 -6.748e+00 1.898e-03 4.974e-05 1.000e+00 1.000e+00 126 -6.222e+00 -6.222e+00 +#> Path [2] :Best Iter: [3] ELBO (-6.220670) evaluations: (126) +#> Path [3] :Initial log joint density = -6.815359 #> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 2.434e-04 9.233e-07 1.000e+00 1.000e+00 101 -6.183e+00 -6.183e+00 -#> Path [3] :Best Iter: [4] ELBO (-6.183157) evaluations: (101) -#> Path [4] :Initial log joint density = -6.999423 +#> 3 -6.748e+00 4.756e-03 9.139e-05 9.421e-01 9.421e-01 76 -6.278e+00 -6.278e+00 +#> Path [3] :Best Iter: [2] ELBO (-6.242127) evaluations: (76) +#> Path [4] :Initial log joint density = -17.875212 #> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 4.925e-04 1.164e-04 9.085e-01 9.085e-01 101 -6.107e+00 -6.107e+00 -#> Path [4] :Best Iter: [4] ELBO (-6.107025) evaluations: (101) -#> Path [5] :Initial log joint density = -17.406798 +#> 5 -6.748e+00 9.541e-04 2.212e-05 1.000e+00 1.000e+00 126 -6.214e+00 -6.214e+00 +#> Path [4] :Best Iter: [5] ELBO (-6.214309) evaluations: (126) +#> Path [5] :Initial log joint density = -6.766198 #> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.234e-03 3.202e-05 1.000e+00 1.000e+00 126 -6.124e+00 -6.124e+00 -#> Path [5] :Best Iter: [5] ELBO (-6.123731) evaluations: (126) -#> Path [6] :Initial log joint density = -6.778306 +#> 3 -6.748e+00 2.751e-03 1.480e-04 1.000e+00 1.000e+00 76 -6.182e+00 -6.182e+00 +#> Path [5] :Best Iter: [3] ELBO (-6.182449) evaluations: (76) +#> Path [6] :Initial log joint density = -17.054272 #> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 1.349e-04 3.452e-07 1.000e+00 1.000e+00 101 -6.229e+00 -6.229e+00 -#> Path [6] :Best Iter: [2] ELBO (-6.102031) evaluations: (101) -#> Path [7] :Initial log joint density = -16.742631 +#> 5 -6.748e+00 1.428e-03 3.932e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00 +#> Path [6] :Best Iter: [2] ELBO (-6.179165) evaluations: (126) +#> Path [7] :Initial log joint density = -18.498779 #> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.582e-03 4.527e-05 1.000e+00 1.000e+00 126 -6.275e+00 -6.275e+00 -#> Path [7] :Best Iter: [3] ELBO (-6.151199) evaluations: (126) -#> Path [8] :Initial log joint density = -9.611781 +#> 5 -6.748e+00 5.714e-04 1.045e-05 1.000e+00 1.000e+00 126 -6.239e+00 -6.239e+00 +#> Path [7] :Best Iter: [2] ELBO (-6.173540) evaluations: (126) +#> Path [8] :Initial log joint density = -9.008917 #> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 5.082e-04 5.350e-06 1.000e+00 1.000e+00 126 -6.224e+00 -6.224e+00 -#> Path [8] :Best Iter: [3] ELBO (-6.204548) evaluations: (126) -#> Path [9] :Initial log joint density = -6.795862 +#> 5 -6.748e+00 3.451e-04 2.910e-06 1.000e+00 1.000e+00 126 -6.200e+00 -6.200e+00 +#> Path [8] :Best Iter: [2] ELBO (-6.176862) evaluations: (126) +#> Path [9] :Initial log joint density = -7.868746 #> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 3 -6.748e+00 3.609e-03 4.044e-05 9.535e-01 9.535e-01 76 -6.173e+00 -6.173e+00 -#> Path [9] :Best Iter: [3] ELBO (-6.172749) evaluations: (76) -#> Path [10] :Initial log joint density = -7.466078 +#> 5 -6.748e+00 1.971e-04 1.133e-06 1.000e+00 1.000e+00 126 -6.234e+00 -6.234e+00 +#> Path [9] :Best Iter: [4] ELBO (-6.211410) evaluations: (126) +#> Path [10] :Initial log joint density = -6.753679 #> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 7.983e-04 1.992e-06 1.000e+00 1.000e+00 126 -6.169e+00 -6.169e+00 -#> Path [10] :Best Iter: [5] ELBO (-6.168690) evaluations: (126) -#> Total log probability function evaluations:1235 -#> Pareto k value (0.97) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. +#> 3 -6.748e+00 9.064e-04 2.757e-05 1.000e+00 1.000e+00 76 -6.257e+00 -6.257e+00 +#> Path [10] :Best Iter: [2] ELBO (-6.157033) evaluations: (76) +#> Total log probability function evaluations:1260 #> Finished in 0.2 seconds. # Specifying initial values as a function diff --git a/docs/reference/model-method-sample_mpi.html b/docs/reference/model-method-sample_mpi.html index 5aab9dce2..6a265288c 100644 --- a/docs/reference/model-method-sample_mpi.html +++ b/docs/reference/model-method-sample_mpi.html @@ -43,7 +43,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -526,7 +526,6 @@

    Examples

    # fit <- mod$sample_mpi(..., mpi_args = list("n" = 4)) # } - diff --git a/docs/reference/model-method-variables.html b/docs/reference/model-method-variables.html index 208a40b12..c30ec812b 100644 --- a/docs/reference/model-method-variables.html +++ b/docs/reference/model-method-variables.html @@ -24,7 +24,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/model-method-variational-1.png b/docs/reference/model-method-variational-1.png index aa185638c..35206a7af 100644 Binary files a/docs/reference/model-method-variational-1.png and b/docs/reference/model-method-variational-1.png differ diff --git a/docs/reference/model-method-variational-2.png b/docs/reference/model-method-variational-2.png index 4cf81fa80..b078570c6 100644 Binary files a/docs/reference/model-method-variational-2.png and b/docs/reference/model-method-variational-2.png differ diff --git a/docs/reference/model-method-variational-3.png b/docs/reference/model-method-variational-3.png index c67b37ed1..35367ed42 100644 Binary files a/docs/reference/model-method-variational-3.png and b/docs/reference/model-method-variational-3.png differ diff --git a/docs/reference/model-method-variational.html b/docs/reference/model-method-variational.html index 68afc88a7..0837588e6 100644 --- a/docs/reference/model-method-variational.html +++ b/docs/reference/model-method-variational.html @@ -27,7 +27,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -413,7 +413,7 @@

    Examples

    # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL) -#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.35.0 +#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.36.0 # Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan @@ -515,8 +515,8 @@

    Examples

    #> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.30 -7.00 0.811 0.344 -8.83 -6.75 1.00 702. 776. -#> 2 theta 0.254 0.238 0.125 0.124 0.0807 0.483 1.00 634. 580. +#> 1 lp__ -7.35 -7.01 0.882 0.353 -9.14 -6.75 1.00 724. 896. +#> 2 theta 0.254 0.239 0.129 0.126 0.0737 0.488 1.00 532. 657. # Check sampling diagnostics fit_mcmc$diagnostic_summary() @@ -527,7 +527,7 @@

    Examples

    #> [1] 0 0 #> #> $ebfmi -#> [1] 1.1148699 0.8012192 +#> [1] 1.1148479 0.7568734 #> # Get posterior draws @@ -537,22 +537,22 @@

    Examples

    #> , , variable = lp__ #> #> chain -#> iteration 1 2 -#> 1 -7.0 -6.8 -#> 2 -7.9 -6.9 -#> 3 -7.4 -6.8 -#> 4 -6.7 -6.8 -#> 5 -6.9 -10.2 +#> iteration 1 2 +#> 1 -7.0 -8.1 +#> 2 -7.9 -7.9 +#> 3 -7.4 -7.0 +#> 4 -6.7 -6.8 +#> 5 -6.9 -6.8 #> #> , , variable = theta #> #> chain -#> iteration 1 2 -#> 1 0.17 0.23 -#> 2 0.46 0.18 -#> 3 0.41 0.28 -#> 4 0.25 0.23 -#> 5 0.18 0.62 +#> iteration 1 2 +#> 1 0.17 0.088 +#> 2 0.46 0.097 +#> 3 0.41 0.167 +#> 4 0.25 0.292 +#> 5 0.18 0.238 #> #> # ... with 995 more iterations @@ -578,11 +578,6 @@

    Examples

    #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. -# For models fit using MCMC, if you like working with RStan's stanfit objects -# then you can create one with rstan::read_stan_csv() -# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) - - # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm) # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") @@ -592,7 +587,7 @@

    Examples

    #> 6 -5.00402 0.000246518 8.73164e-07 1 1 9 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_optim$summary() #> # A tibble: 2 × 2 #> variable estimate @@ -603,12 +598,12 @@

    Examples

    # Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation # to the posterior fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) -#> Initial log joint probability = -6.9339 +#> Initial log joint probability = -19.2814 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes -#> 4 -6.74802 0.000281861 5.43768e-05 0.9238 0.9238 7 +#> 5 -6.74802 0.000163806 1.63613e-06 1 1 8 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance -#> Finished in 0.2 seconds. +#> Finished in 0.1 seconds. fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) #> Calculating Hessian #> Calculating inverse of Cholesky factor @@ -633,14 +628,14 @@

    Examples

    #> iteration: 1700 #> iteration: 1800 #> iteration: 1900 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_laplace$summary() #> # A tibble: 3 × 7 -#> variable mean median sd mad q5 q95 -#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 lp__ -7.25 -6.97 0.728 0.304 -8.74 -6.75 -#> 2 lp_approx__ -0.505 -0.230 0.714 0.313 -2.05 -0.00175 -#> 3 theta 0.271 0.250 0.125 0.120 0.105 0.511 +#> variable mean median sd mad q5 q95 +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 lp__ -7.24 -6.96 0.752 0.292 -8.69 -6.75 +#> 2 lp_approx__ -0.496 -0.208 0.731 0.290 -1.95 -0.00172 +#> 3 theta 0.270 0.248 0.123 0.116 0.0971 0.507 # Run 'variational' method to use ADVI to approximate posterior fit_vb <- mod$variational(data = stan_data, seed = 123) @@ -649,8 +644,8 @@

    Examples

    #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ -#> Gradient evaluation took 1.2e-05 seconds -#> 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds. +#> Gradient evaluation took 1.1e-05 seconds +#> 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) @@ -697,7 +692,7 @@

    Examples

    #> 5 -6.748e+00 2.145e-04 1.301e-06 1.000e+00 1.000e+00 126 -6.197e+00 -6.197e+00 #> Path [4] :Best Iter: [5] ELBO (-6.197118) evaluations: (126) #> Total log probability function evaluations:4379 -#> Finished in 0.1 seconds. +#> Finished in 0.2 seconds. fit_pf$summary() #> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 @@ -714,48 +709,49 @@

    Examples

    fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40, history_size=50, max_lbfgs_iters=100) #> Warning: Number of PSIS draws is larger than the total number of draws returned by the single Pathfinders. This is likely unintentional and leads to re-sampling from the same draws. -#> Path [1] :Initial log joint density = -7.129286 +#> Path [1] :Initial log joint density = -6.777948 #> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 3.516e-03 7.903e-05 1.000e+00 1.000e+00 101 -6.220e+00 -6.220e+00 -#> Path [1] :Best Iter: [4] ELBO (-6.220449) evaluations: (101) -#> Path [2] :Initial log joint density = -17.101498 +#> 4 -6.748e+00 1.327e-04 3.358e-07 1.000e+00 1.000e+00 101 -6.183e+00 -6.183e+00 +#> Path [1] :Best Iter: [4] ELBO (-6.183163) evaluations: (101) +#> Path [2] :Initial log joint density = -8.072775 #> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.403e-03 3.837e-05 1.000e+00 1.000e+00 126 -6.233e+00 -6.233e+00 -#> Path [2] :Best Iter: [3] ELBO (-6.158188) evaluations: (126) -#> Path [3] :Initial log joint density = -7.224287 +#> 5 -6.748e+00 2.399e-04 1.562e-06 1.000e+00 1.000e+00 126 -6.271e+00 -6.271e+00 +#> Path [2] :Best Iter: [4] ELBO (-6.239963) evaluations: (126) +#> Path [3] :Initial log joint density = -9.025342 #> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 2.662e-04 2.842e-07 1.000e+00 1.000e+00 126 -6.221e+00 -6.221e+00 -#> Path [3] :Best Iter: [2] ELBO (-6.190753) evaluations: (126) -#> Path [4] :Initial log joint density = -7.290267 +#> 5 -6.748e+00 3.454e-04 2.916e-06 1.000e+00 1.000e+00 126 -6.285e+00 -6.285e+00 +#> Path [3] :Best Iter: [2] ELBO (-6.207932) evaluations: (126) +#> Path [4] :Initial log joint density = -9.983004 #> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 4 -6.748e+00 5.139e-03 1.484e-04 1.000e+00 1.000e+00 101 -6.253e+00 -6.253e+00 -#> Path [4] :Best Iter: [3] ELBO (-6.223795) evaluations: (101) -#> Path [5] :Initial log joint density = -11.338980 +#> 5 -6.748e+00 6.283e-04 7.448e-06 1.000e+00 1.000e+00 126 -6.267e+00 -6.267e+00 +#> Path [4] :Best Iter: [3] ELBO (-6.107202) evaluations: (126) +#> Path [5] :Initial log joint density = -13.400879 #> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.023e-03 1.591e-05 1.000e+00 1.000e+00 126 -6.195e+00 -6.195e+00 -#> Path [5] :Best Iter: [5] ELBO (-6.194597) evaluations: (126) -#> Path [6] :Initial log joint density = -16.944885 +#> 5 -6.748e+00 1.809e-03 4.509e-05 1.000e+00 1.000e+00 126 -6.206e+00 -6.206e+00 +#> Path [5] :Best Iter: [4] ELBO (-6.199332) evaluations: (126) +#> Path [6] :Initial log joint density = -7.627321 #> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.484e-03 4.148e-05 1.000e+00 1.000e+00 126 -6.227e+00 -6.227e+00 -#> Path [6] :Best Iter: [3] ELBO (-6.204972) evaluations: (126) -#> Path [7] :Initial log joint density = -6.748701 +#> 5 -6.748e+00 1.419e-04 6.650e-07 1.000e+00 1.000e+00 126 -6.197e+00 -6.197e+00 +#> Path [6] :Best Iter: [4] ELBO (-6.176730) evaluations: (126) +#> Path [7] :Initial log joint density = -13.719529 #> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 3 -6.748e+00 1.200e-04 1.293e-06 1.000e+00 1.000e+00 76 -6.259e+00 -6.259e+00 -#> Path [7] :Best Iter: [2] ELBO (-6.149195) evaluations: (76) -#> Path [8] :Initial log joint density = -6.748408 +#> 5 -6.748e+00 1.898e-03 4.974e-05 1.000e+00 1.000e+00 126 -6.198e+00 -6.198e+00 +#> Path [7] :Best Iter: [5] ELBO (-6.198257) evaluations: (126) +#> Path [8] :Initial log joint density = -8.734378 #> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 2 -6.748e+00 1.425e-02 1.473e-04 1.000e+00 1.000e+00 51 -6.207e+00 -6.207e+00 -#> Path [8] :Best Iter: [2] ELBO (-6.206834) evaluations: (51) -#> Path [9] :Initial log joint density = -15.458578 +#> 5 -6.748e+00 3.325e-04 2.705e-06 1.000e+00 1.000e+00 126 -6.215e+00 -6.215e+00 +#> Path [8] :Best Iter: [3] ELBO (-6.210584) evaluations: (126) +#> Path [9] :Initial log joint density = -15.787917 #> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 5 -6.748e+00 1.991e-03 6.002e-05 1.000e+00 1.000e+00 126 -6.285e+00 -6.285e+00 -#> Path [9] :Best Iter: [2] ELBO (-6.192523) evaluations: (126) -#> Path [10] :Initial log joint density = -6.748883 +#> 5 -6.748e+00 1.925e-03 5.805e-05 1.000e+00 1.000e+00 126 -6.251e+00 -6.251e+00 +#> Path [9] :Best Iter: [3] ELBO (-6.246013) evaluations: (126) +#> Path [10] :Initial log joint density = -7.311648 #> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes -#> 3 -6.748e+00 1.441e-04 1.727e-06 1.000e+00 1.000e+00 76 -6.246e+00 -6.246e+00 -#> Path [10] :Best Iter: [3] ELBO (-6.246075) evaluations: (76) -#> Total log probability function evaluations:1185 -#> Finished in 0.1 seconds. +#> 4 -6.748e+00 5.348e-03 1.585e-04 1.000e+00 1.000e+00 101 -6.229e+00 -6.229e+00 +#> Path [10] :Best Iter: [3] ELBO (-6.203261) evaluations: (101) +#> Total log probability function evaluations:1360 +#> Pareto k value (0.78) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor. +#> Finished in 0.2 seconds. # Specifying initial values as a function fit_mcmc_w_init_fun <- mod$sample( @@ -772,7 +768,7 @@

    Examples

    #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. -#> Total execution time: 0.3 seconds. +#> Total execution time: 0.4 seconds. #> fit_mcmc_w_init_fun_2 <- mod$sample( data = stan_data, diff --git a/docs/reference/read_cmdstan_csv.html b/docs/reference/read_cmdstan_csv.html index 37124bfc0..ab7ebbaae 100644 --- a/docs/reference/read_cmdstan_csv.html +++ b/docs/reference/read_cmdstan_csv.html @@ -22,7 +22,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -223,10 +223,10 @@

    Examples

    fit1 <- cmdstanr_example("logistic", method = "sample", save_warmup = TRUE) csv_files <- fit1$output_files() print(csv_files) -#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021545-1-30065a.csv" -#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021545-2-30065a.csv" -#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021545-3-30065a.csv" -#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-202407021545-4-30065a.csv" +#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310849-1-4ed0d7.csv" +#> [2] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310849-2-4ed0d7.csv" +#> [3] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310849-3-4ed0d7.csv" +#> [4] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-202503310849-4-4ed0d7.csv" # Creating fitting model objects @@ -234,9 +234,9 @@

    Examples

    fit2 <- as_cmdstan_fit(csv_files) fit2$print("beta") #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail -#> beta[1] -0.68 -0.67 0.25 0.25 -1.10 -0.27 1.00 3980 2575 -#> beta[2] -0.26 -0.26 0.22 0.22 -0.64 0.09 1.00 4323 3108 -#> beta[3] 0.68 0.67 0.27 0.26 0.26 1.13 1.00 4167 3210 +#> beta[1] -0.66 -0.66 0.25 0.24 -1.07 -0.27 1.00 3828 2751 +#> beta[2] -0.27 -0.27 0.23 0.22 -0.65 0.09 1.00 4397 2844 +#> beta[3] 0.68 0.68 0.27 0.28 0.24 1.13 1.00 4288 2907 # Using read_cmdstan_csv # @@ -246,9 +246,9 @@

    Examples

    #> List of 8 #> $ metadata :List of 42 #> ..$ stan_version_major : num 2 -#> ..$ stan_version_minor : num 35 +#> ..$ stan_version_minor : num 36 #> ..$ stan_version_patch : num 0 -#> ..$ start_datetime : chr "2024-07-02 21:45:26 UTC" +#> ..$ start_datetime : chr "2025-03-31 14:49:26 UTC" #> ..$ method : chr "sample" #> ..$ save_warmup : int 1 #> ..$ thin : num 1 @@ -266,15 +266,15 @@

    Examples

    #> ..$ num_chains : num 1 #> ..$ id : num [1:4] 1 2 3 4 #> ..$ init : num [1:4] 2 2 2 2 -#> ..$ seed : num 2.06e+09 +#> ..$ seed : num 1.72e+09 #> ..$ refresh : num 100 #> ..$ sig_figs : num -1 -#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/logistic-profile-202407021545-1-8f8eaa.csv" +#> ..$ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/logistic-profile-202503310849-1-480225.csv" #> ..$ save_cmdstan_config : int 0 -#> ..$ stanc_version : chr "stanc3 v2.35.0" +#> ..$ stanc_version : chr "stanc3 v2.36.0" #> ..$ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ... #> ..$ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> ..$ step_size_adaptation: num [1:4] 0.753 0.696 0.766 0.757 +#> ..$ step_size_adaptation: num [1:4] 0.785 0.802 0.787 0.715 #> ..$ model_name : chr "logistic_model" #> ..$ adapt_engaged : int 1 #> ..$ adapt_delta : num 0.8 @@ -285,9 +285,9 @@

    Examples

    #> ..$ threads_per_chain : num 1 #> ..$ time :'data.frame': 4 obs. of 4 variables: #> .. ..$ chain_id: num [1:4] 1 2 3 4 -#> .. ..$ warmup : num [1:4] 0.119 0.121 0.128 0.12 -#> .. ..$ sampling: num [1:4] 0.129 0.131 0.122 0.118 -#> .. ..$ total : num [1:4] 0.248 0.252 0.25 0.238 +#> .. ..$ warmup : num [1:4] 0.077 0.077 0.076 0.078 +#> .. ..$ sampling: num [1:4] 0.071 0.074 0.073 0.077 +#> .. ..$ total : num [1:4] 0.148 0.151 0.149 0.155 #> ..$ stan_variable_sizes :List of 4 #> .. ..$ lp__ : num 1 #> .. ..$ alpha : num 1 @@ -299,35 +299,35 @@

    Examples

    #> ..$ total : int NA #> ..$ chains:'data.frame': 4 obs. of 4 variables: #> .. ..$ chain_id: num [1:4] 1 2 3 4 -#> .. ..$ warmup : num [1:4] 0.119 0.121 0.128 0.12 -#> .. ..$ sampling: num [1:4] 0.129 0.131 0.122 0.118 -#> .. ..$ total : num [1:4] 0.248 0.252 0.25 0.238 +#> .. ..$ warmup : num [1:4] 0.077 0.077 0.076 0.078 +#> .. ..$ sampling: num [1:4] 0.071 0.074 0.073 0.077 +#> .. ..$ total : num [1:4] 0.148 0.151 0.149 0.155 #> $ inv_metric :List of 4 -#> ..$ 1: num [1:4] 0.0537 0.06 0.0539 0.075 -#> ..$ 2: num [1:4] 0.0427 0.0583 0.0515 0.083 -#> ..$ 3: num [1:4] 0.0455 0.062 0.0441 0.0752 -#> ..$ 4: num [1:4] 0.0443 0.0707 0.0544 0.0781 +#> ..$ 1: num [1:4] 0.0414 0.0575 0.0508 0.0651 +#> ..$ 2: num [1:4] 0.0478 0.0564 0.0458 0.068 +#> ..$ 3: num [1:4] 0.0464 0.0556 0.0444 0.0718 +#> ..$ 4: num [1:4] 0.0418 0.0554 0.0495 0.078 #> $ step_size :List of 4 -#> ..$ 1: num 0.753 -#> ..$ 2: num 0.696 -#> ..$ 3: num 0.766 -#> ..$ 4: num 0.757 -#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -77.2 -77.2 -77.2 -65.2 -65.2 ... +#> ..$ 1: num 0.785 +#> ..$ 2: num 0.802 +#> ..$ 3: num 0.787 +#> ..$ 4: num 0.715 +#> $ warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -82.8 -82.8 -82.8 -70.9 -67.5 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -67.2 -64.2 -64.3 -65.2 -66.3 ... +#> $ post_warmup_draws : 'draws_array' num [1:1000, 1:4, 1:105] -64.9 -64.9 -66.2 -65.8 -65 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ... -#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 0.749 0 0 0.978 0.989 ... +#> $ warmup_sampler_diagnostics : 'draws_array' num [1:1000, 1:4, 1:6] 6.67e-01 0.00 1.24e-316 9.96e-01 1.00 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" #> .. ..$ variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ... -#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 0.865 1 0.991 0.942 0.714 ... +#> $ post_warmup_sampler_diagnostics: 'draws_array' num [1:1000, 1:4, 1:6] 1 0.983 0.825 0.992 0.992 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> .. ..$ chain : chr [1:4] "1" "2" "3" "4" diff --git a/docs/reference/read_sample_csv.html b/docs/reference/read_sample_csv.html index 9254a250a..1b99583c3 100644 --- a/docs/reference/read_sample_csv.html +++ b/docs/reference/read_sample_csv.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/register_knitr_engine.html b/docs/reference/register_knitr_engine.html index efda0c271..fe4a508e3 100644 --- a/docs/reference/register_knitr_engine.html +++ b/docs/reference/register_knitr_engine.html @@ -20,7 +20,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/set_cmdstan_path.html b/docs/reference/set_cmdstan_path.html index 1a31d8bed..db4659663 100644 --- a/docs/reference/set_cmdstan_path.html +++ b/docs/reference/set_cmdstan_path.html @@ -21,7 +21,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/stan_threads.html b/docs/reference/stan_threads.html index 1875658f4..ebcfa75a1 100644 --- a/docs/reference/stan_threads.html +++ b/docs/reference/stan_threads.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/reference/write_stan_file.html b/docs/reference/write_stan_file.html index efb1bdff8..0645c8402 100644 --- a/docs/reference/write_stan_file.html +++ b/docs/reference/write_stan_file.html @@ -24,7 +24,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -188,7 +188,7 @@

    Examples

    f <- write_stan_file(stan_program) print(f) -#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpiACQ3q/model_7f12fc190dd23b0e462f7d73040dd97e.stan" +#> [1] "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpWzIPg0/model_7f12fc190dd23b0e462f7d73040dd97e.stan" lines <- readLines(f) print(lines) diff --git a/docs/reference/write_stan_json.html b/docs/reference/write_stan_json.html index 92151a5c8..2f027f915 100644 --- a/docs/reference/write_stan_json.html +++ b/docs/reference/write_stan_json.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 @@ -167,13 +167,13 @@

    Examples

    #> "N": 5, #> "K": 2, #> "x": [ -#> [-1.3149626812135, -0.191761909283258], -#> [1.70603877388749, -0.282224274118901], -#> [0.177503683488102, -0.683561574477846], -#> [-0.393377531770713, 0.580194679530021], -#> [0.578306036031158, -1.06993557582488] +#> [0.594273774110513, 0.718888729854143], +#> [0.0591351681787969, 0.251651069028968], +#> [0.413398894737046, 1.35727443615177], +#> [-1.09777217457042, 0.404468471278607], +#> [0.711175257270441, 0.264364269837939] #> ], -#> "y": [10, 9, 8, 12, 11], +#> "y": [10, 11, 13, 11, 12], #> "z": [1, 0] #> } diff --git a/docs/reference/write_stan_tempfile.html b/docs/reference/write_stan_tempfile.html index efc551cde..5891e81a2 100644 --- a/docs/reference/write_stan_tempfile.html +++ b/docs/reference/write_stan_tempfile.html @@ -17,7 +17,7 @@ cmdstanr - 0.8.1 + 0.9.0 diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 93d8ac089..f502670b8 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -30,9 +30,6 @@ https://mc-stan.org/cmdstanr/articles/index.html - - https://mc-stan.org/cmdstanr/articles/opencl.html - https://mc-stan.org/cmdstanr/articles/posterior.html