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| -rw-r--r-- | gnu/packages/cran.scm | 52 |
1 files changed, 52 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index fff71790b6..fb450eb25d 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -21726,3 +21726,55 @@ of R without the need of linking to R code. Rserve supports remote connection, user authentication and file transfer. A simple R client is included in this package as well.") (license license:gpl2))) + +(define-public r-brms + (package + (name "r-brms") + (version "2.12.0") + (source + (origin + (method url-fetch) + (uri (cran-uri "brms" version)) + (sha256 + (base32 + "1699lwkklfhjz7fddawlig552g2zvrc34mqwrzqjgl35r9fm08gs")))) + (properties `((upstream-name . "brms"))) + (build-system r-build-system) + (propagated-inputs + `(("r-abind" ,r-abind) + ("r-backports" ,r-backports) + ("r-bayesplot" ,r-bayesplot) + ("r-bridgesampling" ,r-bridgesampling) + ("r-coda" ,r-coda) + ("r-future" ,r-future) + ("r-ggplot2" ,r-ggplot2) + ("r-glue" ,r-glue) + ("r-loo" ,r-loo) + ("r-matrix" ,r-matrix) + ("r-matrixstats" ,r-matrixstats) + ("r-mgcv" ,r-mgcv) + ("r-nleqslv" ,r-nleqslv) + ("r-nlme" ,r-nlme) + ("r-rcpp" ,r-rcpp) + ("r-rstan" ,r-rstan) + ("r-rstantools" ,r-rstantools) + ("r-shinystan" ,r-shinystan))) + (native-inputs `(("r-knitr" ,r-knitr))) + (home-page + "https://github.com/paul-buerkner/brms") + (synopsis + "Bayesian Regression Models using 'Stan'") + (description + "Fit Bayesian generalized (non-)linear multivariate multilevel models +using 'Stan' for full Bayesian inference. A wide range of distributions and +link functions are supported, allowing users to fit -- among others -- linear, +robust linear, count data, survival, response times, ordinal, zero-inflated, +hurdle, and even self-defined mixture models all in a multilevel context. +Further modeling options include non-linear and smooth terms, auto-correlation +structures, censored data, meta-analytic standard errors, and quite a few +more. In addition, all parameters of the response distribution can be +predicted in order to perform distributional regression. Prior specifications +are flexible and explicitly encourage users to apply prior distributions that +actually reflect their beliefs. Model fit can easily be assessed and compared +with posterior predictive checks and leave-one-out cross-validation.") + (license license:gpl2))) |