“Brms: an R Package for Bayesian Generalized Multivariate Non-Linear Multilevel Models Using Stan”, Paul Bürkner (R, Bayes, survival analysis)⁠:

The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference. The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses.

A wide range of response distributions are supported, allowing users to fit—among others—linear, robust linear, count data, survival, response times, ordinal, zero-inflated, 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, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (ie. models with multiple response variables) can be fit, as well.

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, cross-validation, and Bayes factors.