Merge pull request #12 from nimble-dev/typo_fixes

Typo fixes into master

Author: dochvam

DESCRIPTION

@@ -1,16 +1,16 @@
 Package: nimbleEcology
 Type: Package
 Title: Distributions for Ecological Models in 'nimble'
-Version: 0.2.0
+Version: 0.2.1
 Maintainer: Benjamin R. Goldstein <ben.goldstein@berkeley.edu>
 Authors@R: c(person("Benjamin R.", "Goldstein", role = c("aut", "cre"),
                      email = "ben.goldstein@berkeley.edu"),
               person("Daniel", "Turek", role = "aut"),
               person("Lauren", "Ponisio", role = "aut"),
               person("Perry", "de Valpine", role = "aut"))
-Date: 2020-02-20
+Date: 2020-04-03
 Description: Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
-  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, and dynamic hidden Markov models.
+  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models.
   (Jolly (1965) <doi:10.2307/2333826>, Seber (1965) <10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).
 License: GPL-3
 Copyright: Copyright (c) 2019, Perry de Valpine, Ben Goldstein, Daniel Turek, Lauren Ponisio

R/dCJS.R

@@ -117,8 +117,8 @@
 #'
 #' # Define code for a nimbleModel
 #' nc <- nimbleCode({
-#'   x[1:4] ~ dCJS_vs(probSurvive, probCapture, len = 4)
-#'   probSurvive ~ dunif(0,1)
+#'   x[1:4] ~ dCJS_vs(probSurvive[1:4], probCapture, len = 4)
+#'   probCapture ~ dunif(0,1)
 #'   for (i in 1:4) probSurvive[i] ~ dunif(0, 1)
 #' })
 #'

R/dDHMM.R

@@ -131,19 +131,19 @@
 #' # Define code for a nimbleModel
 #'  nc <- nimbleCode({
 #'    x[1:4] ~ dDHMM(init[1:3], probObs = probObs[1:3, 1:2],
-#'                   probTrans = probTrans[1:3, 1:3, 1:4], len = 4)
+#'                   probTrans = probTrans[1:3, 1:3, 1:3], len = 4)
 #'
 #'    for (i in 1:3) {
 #'      init[i] ~ dunif(0,1)
 #'
 #'      for (j in 1:3) {
-#'        for (t in 1:4) {
+#'        for (t in 1:3) {
 #'          probTrans[i,j,t] ~ dunif(0,1)
 #'        }
 #'      }
 #'
 #'      probObs[i, 1] ~ dunif(0,1)
-#'      probObs[i, 2] <- 1 - probObs[1,i]
+#'      probObs[i, 2] <- 1 - probObs[i,1]
 #'    }
 #'  })
 #'

R/dDynOcc.R

@@ -133,24 +133,25 @@
 #' @examples
 #' \donttest{
 #' # Set up constants and initial values for defining the model
-#'   x <- matrix(c(0,0,NA,0,
+#'   x <- matrix(c(0,0,0,0,
 #'                 1,1,1,0,
 #'                 0,0,0,0,
 #'                 0,0,1,0,
-#'                 0,0,0,NA), nrow = 4)
-#'   start <- c(1,1,2,1)
-#'   end <- c(5,5,5,4)
+#'                 0,0,0,0), nrow = 4)
+#'   start <- c(1,1,2,1,1)
+#'   end <- c(5,5,5,4,5)
 #'   init <- 0.7
 #'   probPersist <- 0.5
 #'   probColonize <- 0.2
-#'   p <- 0.8
+#'   p <- matrix(rep(0.5, 20), nrow = 4)
 #'
 #'
 #' # Define code for a nimbleModel
 #'  nc <- nimbleCode({
 #'
-#'    x[1:2, 1:5] ~ dDynOcc_vvm(nrep[1:2], init,
-#'    probPersist[1:2], probColonize[1:2], p[1:2,1:5])
+#'    x[1:2, 1:5] ~ dDynOcc_vvm(init,
+#'      probPersist[1:2], probColonize[1:2], p[1:2,1:5],
+#'      start = start[1:4], end = end[1:4])
 #'
 #'    init ~ dunif(0,1)
 #'
@@ -167,12 +168,13 @@
 #'  })
 #'
 #' # Build the model, providing data and initial values
-#' DynOcc_model <- nimbleModel(nc, data = list(x = dat, nrep = nrep),
+#' DynOcc_model <- nimbleModel(nc, data = list(x = x),
+#'                             constants = list(start = start, end = end),
 #'                             inits = list(p = p, probPersist = probPersist,
 #'                                          init = init, probColonize = probColonize))
 #'
 #' # Calculate log probability of data from the model
-#' DynOcc_model$calculate()
+#' DynOcc_model$calculate("x")
 #' # Use the model for a variety of other purposes...
 #' }
 

R/dHMM.R

@@ -137,18 +137,16 @@
 #'
 #' # Define code for a nimbleModel
 #'  nc <- nimbleCode({
-#'    x[1:5] ~ dHMM(init[1:3], probObs = probObs[1:2,1:3],
+#'    x[1:5] ~ dHMM(init[1:3], probObs = probObs[1:3,1:2],
 #'                  probTrans = probTrans[1:3, 1:3], len = 5)
 #'
 #'    for (i in 1:3) {
-#'      init[i] ~ dunif(0,1)
-#'
 #'      for (j in 1:3) {
 #'        probTrans[i,j] ~ dunif(0,1)
 #'      }
 #'
 #'      probObs[i, 1] ~ dunif(0,1)
-#'      probObs[i, 2] <- 1 - probObs[1,i]
+#'      probObs[i, 2] <- 1 - probObs[i, 1]
 #'    }
 #'  })
 #'

R/dNmixture.R

@@ -146,7 +146,7 @@
 #'                     inits = list(lambda = lambda,
 #'                                  prob = prob))
 #' # Calculate log probability of data from the model
-#' nmix_model$calculate()
+#' nmix$calculate()
 #' # Use the model for a variety of other purposes...
 #' }
 
@@ -209,7 +209,7 @@ dNmixture_s <- nimbleFunction(
                    Nmax = double(0, default = -1),
                    len = double(),
                    log = integer(0, default = 0)) {
-    if (length(x) != len) stop("in dNmixture_v, len must equal length(x).")
+    if (length(x) != len) stop("in dNmixture_s, len must equal length(x).")
 
     # Lambda cannot be negative
     if (lambda < 0) {