Changes for cran 226

rstan/rstan/DESCRIPTION

@@ -1,10 +1,11 @@
 Package: rstan
 Type: Package
 Title: R Interface to Stan
-Version: 2.26.22
+Version: 2.26.23
 Authors@R: c(person("Jiqiang", "Guo", email = "[email protected]", role = "aut"),
              person("Jonah", "Gabry", email = "[email protected]", role = "aut"),
              person("Ben", "Goodrich", email = "[email protected]", role = c("cre", "aut")),
+             person("Andrew", "Johnson", email = "[email protected]", role = "aut"),
              person("Sebastian", "Weber", email = "[email protected]", role = "aut"),
              person("Daniel", "Lee", role = "ctb"),
              person("Krzysztof", "Sakrejda", role = "ctb"),
@@ -62,7 +63,9 @@ Suggests:
     rstantools,
     rstudioapi,
     Matrix,
-    knitr
+    knitr,
+    rstanarm,
+    coda
 URL: https://mc-stan.org/rstan/, https://discourse.mc-stan.org
 BugReports: https://github.com/stan-dev/rstan/issues/
 VignetteBuilder: knitr

rstan/rstan/inst/CITATION

@@ -1,6 +1,6 @@
 citHeader("To cite RStan in publications use:")
 
-citEntry(entry = "Misc", 
+bibentry(bibtype = "Misc",
          title = "{RStan}: the {R} interface to {Stan}",
          author = person(given = "Stan Development Team"),
          note = paste("R package version", meta$Version),

rstan/rstan/man/rstan.Rd

@@ -1,7 +1,7 @@
 
 \name{rstan-package}
 \alias{rstan-package}
-\alias{rstan} 
+\alias{rstan}
 \docType{package}
 \title{RStan --- the \R interface to Stan}
 \description{
@@ -13,47 +13,47 @@
   RStan is the \R interface to the \href{https://mc-stan.org/}{Stan} C++ package.
   The RStan interface (\pkg{rstan} \R package) provides:
   \itemize{
-  \item Full Bayesian inference using the No-U-Turn sampler (NUTS), 
+  \item Full Bayesian inference using the No-U-Turn sampler (NUTS),
     a variant of Hamiltonian Monte Carlo (HMC)
-  \item Approximate Bayesian inference using automatic differentiation 
+  \item Approximate Bayesian inference using automatic differentiation
     variational inference (ADVI)
   \item Penalized maximum likelihood estimation using L-BFGS optimization
   }
-  
+
   For documentation on Stan itself, including the manual and user guide
   for the modeling language, case studies and worked examples, and other
   tutorial information visit the Users section of the Stan website:
-  
+
   \itemize{
   \item \href{https://mc-stan.org/users/documentation/}{mc-stan.org/users/documentation}
   }
-  
+
 }
 \section{Other \R packages from the Stan Development Team}{
   Various related \R packages are also available from the Stan Development Team including these and more:
   \tabular{llll}{
   \strong{Package} \tab \strong{Description} \tab \strong{Doc} \tab \strong{Website} \cr
-  \pkg{bayesplot} 
-    \tab ggplot-based plotting of parameter estimates, diagnostics, and posterior predictive checks. 
+  \pkg{bayesplot}
+    \tab ggplot-based plotting of parameter estimates, diagnostics, and posterior predictive checks.
     \tab \link[bayesplot]{bayesplot-package}
     \tab \href{https://mc-stan.org/bayesplot/}{mc-stan.org/bayesplot}
     \cr
-  \pkg{shinystan} 
-    \tab Interactive GUI for exploring MCMC output. 
+  \pkg{shinystan}
+    \tab Interactive GUI for exploring MCMC output.
     \tab \link[shinystan]{shinystan-package}
     \tab \href{https://mc-stan.org/shinystan/}{mc-stan.org/shinystan}
     \cr
-  \pkg{loo} 
+  \pkg{loo}
     \tab Out-of-sample predictive performance estimates and model comparison.
-    \tab \link[loo]{loo-package} 
+    \tab \link[loo]{loo-package}
     \tab \href{https://mc-stan.org/loo/}{mc-stan.org/loo}
     \cr
-  \pkg{rstanarm} 
-    \tab \R formula interface for applied regression modeling. 
+  \pkg{rstanarm}
+    \tab \R formula interface for applied regression modeling.
     \tab \link[rstanarm]{rstanarm-package}
     \tab \href{https://mc-stan.org/rstanarm/}{mc-stan.org/rstanarm}
-    \cr 
-  \pkg{rstantools} 
+    \cr
+  \pkg{rstantools}
     \tab Tools for developers of \R packages interfacing with Stan.
     \tab \link[rstantools]{rstantools-package}
     \tab \href{https://mc-stan.org/rstantools/}{mc-stan.org/rstantools}
@@ -64,22 +64,22 @@
   \tabular{ll}{
     Jonah Gabry (author)\tab <[email protected]> \cr
     Ben Goodrich (maintainer, author)\tab <[email protected]> \cr
-    Jiqiang Guo (author)\tab <[email protected]> \cr 
+    Jiqiang Guo (author)\tab <[email protected]> \cr
   }
 
-  There are also many other important contributors to RStan 
-  (\href{https://github.com/stan-dev/rstan}{github.com/rstan}). 
-  Please use 'Stan Development Team' whenever citing the R interface to Stan. 
+  There are also many other important contributors to RStan
+  (\href{https://github.com/stan-dev/rstan}{github.com/rstan}).
+  Please use 'Stan Development Team' whenever citing the R interface to Stan.
   A BibTex entry is available from \url{https://mc-stan.org/rstan/authors}
   or \code{citation("rstan")}.
 }
 
 \seealso{
 \itemize{
   \item The RStan vignettes: \url{https://mc-stan.org/rstan/articles/}.
-  \item \code{\link{stan}} for details on fitting models and 
+  \item \code{\link{stan}} for details on fitting models and
   \code{\linkS4class{stanfit}} for information on the fitted model objects.
-  \item The \code{\link{lookup}} for finding a function in the Stan language 
+  \item The \code{\link{lookup}} for finding a function in the Stan language
   that corresponds to a \R function or name.
   \item \url{https://github.com/stan-dev/rstan/issues/} to submit a bug
   report or feature request.
@@ -88,40 +88,40 @@
 }
 }
 
-\examples{\dontrun{ 
+\examples{\dontrun{
 
 stanmodelcode <- "
 data {
   int<lower=0> N;
   real y[N];
-} 
+}
 
 parameters {
   real mu;
-} 
+}
 
 model {
   target += normal_lpdf(mu | 0, 10);
   target += normal_lpdf(y  | mu, 1);
-} 
+}
 "
 
-y <- rnorm(20) 
-dat <- list(N = 20, y = y); 
-fit <- stan(model_code = stanmodelcode, model_name = "example", 
+y <- rnorm(20)
+dat <- list(N = 20, y = y);
+fit <- stan(model_code = stanmodelcode, model_name = "example",
             data = dat, iter = 2012, chains = 3, verbose = TRUE,
-            sample_file = file.path(tempdir(), 'norm.csv')) 
+            sample_file = file.path(tempdir(), 'norm.csv'))
 print(fit)
 
-# extract samples 
-e <- extract(fit, permuted = FALSE) # return a list of arrays 
+# extract samples
+e <- extract(fit, permuted = FALSE) # return a list of arrays
 str(e)
 
-arr <- as.array(fit) # return an array 
+arr <- as.array(fit) # return an array
 str(arr)
 
 mat <- as.matrix(fit) # return a matrix
 str(mat)
 }}
-\keyword{rstan} 
+\keyword{rstan}
 \keyword{package}

rstan/rstan/man/sbc.Rd

@@ -4,7 +4,7 @@
 \alias{plot.sbc}
 \title{Simulation Based Calibration (sbc)}
 \description{
-\if{html}{\figure{stanlogo.png}{options: width="25px" alt="https://mc-stan.org/about/logo/"}}
+\if{html}{\figure{stanlogo.png}{options: width="25" alt="https://mc-stan.org/about/logo/"}}
 Check whether a model is well-calibrated with respect to the
 prior distribution and hence possibly amenable to obtaining
 a posterior distribution conditional on observed data.
@@ -19,8 +19,8 @@ a posterior distribution conditional on observed data.
   \item{stanmodel}{
     An object of \code{\link{stanmodel-class}} that is first created by
     calling the \code{\link{stan_model}} function}
-  \item{data}{A named \code{list} or \code{environment} providing the data for 
-    the model, or a character vector for all the names of objects to use as data. 
+  \item{data}{A named \code{list} or \code{environment} providing the data for
+    the model, or a character vector for all the names of objects to use as data.
     This is the same format as in \code{\link{stan}} or \code{\link{sampling}}.}
   \item{M}{
     The number of times to condition on draws from the prior predictive
@@ -38,7 +38,7 @@ a posterior distribution conditional on observed data.
     whatever runs have been saved to disk and ignore argument \code{M}.}
 }
 \details{
-  This function assumes adherence to the following conventions in the 
+  This function assumes adherence to the following conventions in the
   underlying Stan program:
   \enumerate{
   \item Realizations of the unknown parameters are drawn in the \code{transformed data}
@@ -53,22 +53,22 @@ a posterior distribution conditional on observed data.
     a trailing underscore.
   \item The realizations of the unknown parameters should be copied into a \code{vector}
     in the \code{generated quantities} block named \code{pars_}.
-  \item The realizations from the prior predictive distribution should be copied 
+  \item The realizations from the prior predictive distribution should be copied
     into an object (of the same type) in the \code{generated quantities} block
     named \code{y_}. Technically, this step is optional and could be omitted
     to conserve RAM, but inspecting the realizations from the prior predictive distribution
     is a good way to judge whether the priors are reasonable.
-  \item The \code{generated quantities} block must contain an integer array named 
-    \code{ranks_} whose only values are zero or one, depending on whether the realization of a 
-    parameter from the posterior distribution exceeds the corresponding \dQuote{true} 
+  \item The \code{generated quantities} block must contain an integer array named
+    \code{ranks_} whose only values are zero or one, depending on whether the realization of a
+    parameter from the posterior distribution exceeds the corresponding \dQuote{true}
     realization, such as \code{theta > theta_;}. These are not actually "ranks"
     but can be used afterwards to reconstruct (thinned) ranks.
   \item The \code{generated quantities} block may contain a vector named \code{log_lik}
     whose values are the contribution to the log-likelihood by each observation. This
     is optional but facilitates calculating Pareto k shape parameters to judge whether
     the posterior distribution is sensitive to particular observations.
   }
-  
+
   Although the user can pass additional arguments to \code{\link{sampling}} through the \dots,
   the following arguments are hard-coded and should not be passed through the \dots:
   \enumerate{
@@ -82,40 +82,40 @@ a posterior distribution conditional on observed data.
     finished, as is done by the \code{thin} argument to the \code{plot} method in order to
     make the histograms consist of approximately independent realizations
   }
-  Other arguments will take the default values used by \code{\link{sampling}} unless 
+  Other arguments will take the default values used by \code{\link{sampling}} unless
   passed through the \dots. Specifying \code{refresh = 0} is recommended to avoid printing
-  a lot of intermediate progress reports to the screen. It may be necessary to pass a 
+  a lot of intermediate progress reports to the screen. It may be necessary to pass a
   list to the \code{control} argument of \code{\link{sampling}} with elements \code{adapt_delta}
   and / or \code{max_treedepth} in order to obtain adequate results.
-  
+
   Ideally, users would want to see the absence of divergent transitions (which is shown
-  by the \code{print} method) and other warnings, plus an approximately uniform histogram 
-  of the ranks for each parameter (which are shown by the \code{plot} method). See the 
+  by the \code{print} method) and other warnings, plus an approximately uniform histogram
+  of the ranks for each parameter (which are shown by the \code{plot} method). See the
   vignette for more details.
 }
 \value{
   The \code{sbc} function outputs a list of S3 class \code{"sbc"}, which contains the
   following elements:
   \enumerate{
-  \item \code{ranks} A list of \code{M} matrices, each with number of 
-    rows equal to the number of saved iterations and number of columns equal to 
+  \item \code{ranks} A list of \code{M} matrices, each with number of
+    rows equal to the number of saved iterations and number of columns equal to
     the number of unknown parameters. These matrices contain the realizations
     of the \code{ranks_} object from the \code{generated quantities} block of the
     Stan program.
   \item \code{Y} If present, a matrix of realizations from the prior predictive
     distribution whose rows are equal to the number of observations and whose columns
-    are equal to \code{M}, which are taken from the \code{y_} object in the 
+    are equal to \code{M}, which are taken from the \code{y_} object in the
     \code{generated quantities} block of the Stan program.
   \item \code{pars} A matrix of realizations from the prior distribution whose rows
-    are equal to the number of parameters and whose columns are equal to \code{M}, 
+    are equal to the number of parameters and whose columns are equal to \code{M},
     which are taken from the \code{pars_} object in the \code{generated quantities}
     block of the Stan program.
   \item \code{pareto_k} A matrix of Pareto k shape parameter estimates or \code{NULL}
     if there is no \code{log_lik} symbol in the \code{generated quantities} block
     of the Stan program
   \item \code{sampler_params} A three-dimensional array that results from combining
     calls to \code{\link{get_sampler_params}} for each of
-    the \code{M} runs. The resulting matrix has rows equal to the number of 
+    the \code{M} runs. The resulting matrix has rows equal to the number of
     post-warmup iterations, columns equal to six, and \code{M} floors. The columns
     are named \code{"accept_stat__"}, \code{"stepsize__"}, \code{"treedepth__"},
     \code{"n_leapfrog__"}, \code{"divergent__"}, and \code{"energy__"}. The most
@@ -132,8 +132,8 @@ a posterior distribution conditional on observed data.
 
 
 \references{
-  The Stan Development Team 
-  \emph{Stan Modeling Language User's Guide and Reference Manual}. 
+  The Stan Development Team
+  \emph{Stan Modeling Language User's Guide and Reference Manual}.
   \url{https://mc-stan.org}.
 
   Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018).