gnu: Add r-rstan.

* gnu/packages/cran.scm (r-rstan): New variable.

gnu/packages/cran.scm

@@ -20469,3 +20469,54 @@ (define-public r-loo
 The package also provides methods for using stacking and other model weighting
 techniques to average Bayesian predictive distributions.")
     (license license:gpl3+)))
+
+(define-public r-rstan
+  (package
+    (name "r-rstan")
+    (version "2.19.3")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (cran-uri "rstan" version))
+       (sha256
+        (base32
+         "128ndwjrhf8b1qvvqz4bl13qlm8718z9qs5ryc6gsdr3vk65s0np"))))
+    (properties `((upstream-name . "rstan")))
+    (build-system r-build-system)
+    (arguments
+     `(#:phases
+       (modify-phases %standard-phases
+         (add-before 'install 'set-timezone
+           ;; This package is picky about timezones.
+           (lambda* (#:key inputs #:allow-other-keys)
+             (setenv "TZ" "UTC+1")
+             (setenv "TZDIR"
+                     (string-append (assoc-ref inputs "tzdata")
+                                    "/share/zoneinfo"))
+             #t)))))
+    (native-inputs
+     `(("tzdata" ,tzdata)
+       ("pandoc" ,ghc-pandoc)))
+    (propagated-inputs
+     `(("r-bh" ,r-bh)
+       ("r-ggplot2" ,r-ggplot2)
+       ("r-gridextra" ,r-gridextra)
+       ("r-inline" ,r-inline)
+       ("r-loo" ,r-loo)
+       ("r-pkgbuild" ,r-pkgbuild)
+       ("r-rcpp" ,r-rcpp)
+       ("r-rcppeigen" ,r-rcppeigen)
+       ("r-stanheaders" ,r-stanheaders)))
+    (home-page "https://discourse.mc-stan.org/")
+    (synopsis "R interface to Stan")
+    (description
+     "User-facing R functions are provided to parse, compile, test, estimate,
+and analyze Stan models by accessing the header-only Stan library provided by
+the StanHeaders package.  The Stan project develops a probabilistic
+programming language that implements full Bayesian statistical inference via
+Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
+approximation, and (optionally penalized) maximum likelihood estimation via
+optimization.  In all three cases, automatic differentiation is used to
+quickly and accurately evaluate gradients without burdening the user with the
+need to derive the partial derivatives.")
+    (license license:gpl3+)))