Package: brms Encoding: UTF-8 Type: Package Title: Bayesian Regression Models using 'Stan' Version: 2.21.0 Date: 2024-03-18 Authors@R: c(person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com", role = c("aut", "cre")), person("Jonah", "Gabry", role = c("ctb")), person("Sebastian", "Weber", role = c("ctb")), person("Andrew", "Johnson", role = c("ctb")), person("Martin", "Modrak", role = c("ctb")), person("Hamada S.", "Badr", role = c("ctb")), person("Frank", "Weber", role = c("ctb")), person("Aki", "Vehtari", role = c("ctb")), person("Mattan S.", "Ben-Shachar", role = c("ctb")), person("Hayden", "Rabel", role = c("ctb")), person("Simon C.", "Mills", role = c("ctb")), person("Stephen", "Wild", role = c("ctb")), person("Ven", "Popov", role = c("ctb"))) Depends: R (>= 3.6.0), Rcpp (>= 0.12.0), methods Imports: rstan (>= 2.29.0), ggplot2 (>= 2.0.0), loo (>= 2.3.1), posterior (>= 1.0.0), Matrix (>= 1.1.1), mgcv (>= 1.8-13), rstantools (>= 2.1.1), bayesplot (>= 1.5.0), bridgesampling (>= 0.3-0), glue (>= 1.3.0), rlang (>= 1.0.0), future (>= 1.19.0), future.apply (>= 1.0.0), matrixStats, nleqslv, nlme, coda, abind, stats, utils, parallel, grDevices, backports Suggests: testthat (>= 0.9.1), emmeans (>= 1.4.2), cmdstanr (>= 0.5.0), projpred (>= 2.0.0), shinystan (>= 2.4.0), splines2 (>= 0.5.0), RWiener, rtdists, extraDistr, processx, mice, spdep, mnormt, lme4, MCMCglmm, ape, arm, statmod, digest, diffobj, R.rsp, gtable, shiny, knitr, rmarkdown 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 both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, 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 prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) ; Bürkner (2018) ; Bürkner (2021) ; Carpenter et al. (2017) . LazyData: true NeedsCompilation: no License: GPL-2 URL: https://github.com/paul-buerkner/brms, https://discourse.mc-stan.org/, https://paul-buerkner.github.io/brms/ BugReports: https://github.com/paul-buerkner/brms/issues Additional_repositories: https://mc-stan.org/r-packages/ VignetteBuilder: knitr, R.rsp RoxygenNote: 7.3.1 Packaged: 2024-03-19 20:40:33 UTC; paul.buerkner Author: Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb], Hamada S. Badr [ctb], Frank Weber [ctb], Aki Vehtari [ctb], Mattan S. Ben-Shachar [ctb], Hayden Rabel [ctb], Simon C. Mills [ctb], Stephen Wild [ctb], Ven Popov [ctb] Maintainer: Paul-Christian Bürkner Repository: CRAN Date/Publication: 2024-03-20 12:30:08 UTC