person("Mattan S.", "Ben-Shachar", role = c("ctb")),
person("Hayden", "Rabel", role = c("ctb")))
projpred (>= 2.0.0), RWiener, rtdists, extraDistr, processx,
mice, spdep, mnormt, lme4, MCMCglmm, splines2, ape, arm,
statmod, digest, diffobj, R.rsp, gtable, shiny, knitr,
Packaged: 2022-04-11 08:20:38 UTC; paul.buerkner

person("Hayden", "Rabel", role = c("ctb")),
person("Simon C.", "Mills", role = c("ctb")))
Suggests: testthat (>= 0.9.1), emmeans (>= 1.4.2), cmdstanr (>= 0.5.0),
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 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.
Packaged: 2022-09-19 11:56:34 UTC; paul.buerkner

1.8-13), rstantools (>= 1.3.0), bayesplot (>= 1.5.0), shinystan
Suggests: testthat (>= 0.9.1), RWiener, future, mice, spdep, mnormt,
lme4, MCMCglmm, ape, arm, R.rsp, knitr, rmarkdown

lme4, MCMCglmm, ape, arm, digest, R.rsp, knitr, rmarkdown

nlme, coda, abind, stats, utils, parallel, grDevices, backports
lme4, MCMCglmm, ape, arm, statmod, digest, R.rsp, knitr,

MCMCglmm, ape, arm, statmod, digest, R.rsp, knitr, rmarkdown

Title: Bayesian Marginal Effects for 'brms' Models
Description: Calculate Bayesian marginal effects and average marginal effects for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6>).
Imports: methods, stats, data.table (>= 1.12.0), extraoperators (>=
0.1.1), brms, bayestestR, Rcpp, posterior
Suggests: testthat (>= 3.0.0), covr, withr, knitr, rmarkdown, margins,

Description: Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).
Author: Joshua F. Wiley [aut, cre] (<https://orcid.org/0000-0002-0271-6702>),