Link extended methods to original

Author: sethaxen

ext/InferenceObjectsMCMCDiagnosticToolsExt/bfmi.jl

@@ -3,6 +3,8 @@
     bfmi(sample_stats::Dataset) -> DimArray
 
 Calculate the chainwise estimated Bayesian fraction of missing information (BFMI).
+
+See [`MCMCDiagnosticTools.bfmi`](@extref) for more details.
 """
 function MCMCDiagnosticTools.bfmi(data::InferenceObjects.InferenceData)
     return MCMCDiagnosticTools.bfmi(data.sample_stats)

ext/InferenceObjectsMCMCDiagnosticToolsExt/ess_rhat.jl

@@ -3,6 +3,9 @@
     ess(data::Dataset; kwargs...) -> Dataset
 
 Calculate the effective sample size (ESS) for each parameter in the data.
+
+For more details and a description of the `kwargs`, see
+[`MCMCDiagnosticTools.ess`](@extref).
 """
 function MCMCDiagnosticTools.ess(data::InferenceObjects.InferenceData; kwargs...)
     return MCMCDiagnosticTools.ess(data.posterior; kwargs...)
@@ -13,6 +16,9 @@ end
     rhat(data::Dataset; kwargs...) -> Dataset
 
 Calculate the ``\\widehat{R}`` diagnostic for each parameter in the data.
+
+For more details and a description of the `kwargs`, see
+[`MCMCDiagnosticTools.rhat`](@extref).
 """
 function MCMCDiagnosticTools.rhat(data::InferenceObjects.InferenceData; kwargs...)
     return MCMCDiagnosticTools.rhat(data.posterior; kwargs...)
@@ -35,6 +41,9 @@ end
 
 Calculate the effective sample size (ESS) and ``\\widehat{R}`` diagnostic for each parameter
 in the data.
+
+For more details and a description of the `kwargs`, see
+[`MCMCDiagnosticTools.ess_rhat`](@extref).
 """
 function MCMCDiagnosticTools.ess_rhat(data::InferenceObjects.InferenceData; kwargs...)
     return MCMCDiagnosticTools.ess_rhat(data.posterior; kwargs...)

ext/InferenceObjectsMCMCDiagnosticToolsExt/mcse.jl

@@ -3,6 +3,9 @@
     mcse(data::Dataset; kwargs...) -> Dataset
 
 Calculate the Monte Carlo standard error (MCSE) for each parameter in the data.
+
+For more details and a description of the `kwargs`, see
+[`MCMCDiagnosticTools.mcse`](@extref).
 """
 function MCMCDiagnosticTools.mcse(data::InferenceObjects.InferenceData; kwargs...)
     return MCMCDiagnosticTools.mcse(data.posterior; kwargs...)

ext/InferenceObjectsMCMCDiagnosticToolsExt/rstar.jl

@@ -7,6 +7,9 @@
     )
 
 Calculate the ``R^*`` diagnostic for the data.
+
+For a description of the `classifier` and `kwargs`, see
+[`MCMCDiagnosticTools.rstar`](@extref).
 """
 function MCMCDiagnosticTools.rstar(
     rng::Random.AbstractRNG, clf, data::InferenceObjects.InferenceData; kwargs...

ext/InferenceObjectsPosteriorStatsExt/hdi.jl

@@ -10,6 +10,8 @@ end
     hdi(data::Dataset; kwargs...) -> Dataset
 
 Calculate the highest density interval (HDI) for each parameter in the data.
+
+For more details and a description of the `kwargs`, see [`PosteriorStats.hdi`](@extref).
 """
 function PosteriorStats.hdi(data::InferenceObjects.InferenceData; kwargs...)
     return PosteriorStats.hdi(data.posterior; kwargs...)

ext/InferenceObjectsPosteriorStatsExt/loo.jl

@@ -6,6 +6,8 @@ Compute PSIS-LOO from log-likelihood values in `data`.
 
 If more than one log-likelihood variable is present, then `var_name` must be provided.
 
+For more details and a description of the `kwargs`, see [`PosteriorStats.loo`](@extref).
+
 # Examples
 
 Calculate PSIS-LOO of a model:

ext/InferenceObjectsPosteriorStatsExt/loo_pit.jl

@@ -9,7 +9,10 @@ Compute LOO-PIT values using existing normalized log LOO importance weights.
     the only observed data variable is used.
   - `y_pred_name`: Name of posterior predictive variable in `idata.posterior_predictive`.
     If not provided, then `y_name` is used.
-  - `kwargs`: Remaining keywords are forwarded to the base method of `loo_pit`.
+  - `kwargs`: Remaining keywords are forwarded to the base method
+    [`PosteriorStats.loo_pit`](@extref).
+
+See [`PosteriorStats.loo_pit`](@extref) for more details.
 
 # Examples
 
@@ -69,7 +72,9 @@ Compute LOO-PIT from groups in `idata` using PSIS-LOO.
     _likelihood_ values. If an array, it must have the same data dimensions as the
     corresponding log-likelihood variable. If not provided, then this is estimated using
     `ess`.
-  - `kwargs`: Remaining keywords are forwarded to the base method of `loo_pit`.
+  - `kwargs`: Remaining keywords are forwarded to [`PosteriorStats.loo_pit`](@extref).
+
+See [`PosteriorStats.loo_pit`](@extref) for more details.
 
 # Examples
 

ext/InferenceObjectsPosteriorStatsExt/r2_score.jl

@@ -10,6 +10,8 @@ Compute ``R²`` from `idata`, automatically formatting the predictions to the co
   - `y_pred_name`: Name of posterior predictive variable in `idata.posterior_predictive`.
     If not provided, then `y_name` is used.
 
+See [`PosteriorStats.r2_score`](@extref) for more details.
+
 # Examples
 
 ```jldoctest

ext/InferenceObjectsPosteriorStatsExt/summarize.jl

@@ -2,7 +2,8 @@
     summarystats(data::InferenceData; group=:posterior, kwargs...) -> SummaryStats
     summarystats(data::Dataset; kwargs...) -> SummaryStats
 
-Compute default summary statistics for the data using `summarize`.
+Compute default summary statistics for the data using
+[`PosteriorStats.summarize`](@ref PosteriorStats.summarize(::InferenceData)).
 """
 function StatsBase.summarystats(data::InferenceObjects.InferenceData; kwargs...)
     return PosteriorStats.summarize(data; kwargs...)
@@ -18,7 +19,7 @@ end
 Compute summary statistics for the data using the provided functions.
 
 For verbose variable labels, provide `compat_labels=false`. For details on `stats_funs` and
-`kwargs`, see the main `summarize` method.
+`kwargs`, see [`PosteriorStats.summarize`](@extref).
 
 # Examples
 

ext/InferenceObjectsPosteriorStatsExt/waic.jl

@@ -6,6 +6,8 @@ Compute WAIC from log-likelihood values in `data`.
 
 If more than one log-likelihood variable is present, then `var_name` must be provided.
 
+See [`PosteriorStats.waic`](@extref) for more details.
+
 # Examples
 
 Calculate WAIC of a model: