paul-buerkner/brms v2.2.0 on GitHub

new features

Specify custom response distributions with function custom_family. (#381)
Model missing values and measurement error in responses using the mi addition term. (#27, #343)
Allow missing values in predictors using mi terms on the right-hand side of model formulas. (#27)
Model interactions between the special predictor terms mo, me, and mi. (#313)
Introduce methods model_weights and loo_model_weights providing several options to compute model weights. (#268)
Introduce method posterior_average to extract posterior samples averaged across models. (#386)
Allow hyperparameters of group-level effects to vary over the levels of a categorical covariate using argument by in function gr. (#365)
Allow predictions of measurement-error models with new data. (#335)
Pass user-defined variables to Stan via stanvar. (#219, #357)
Allow ordinal families in mixture models. (#389)
Model covariates in multi-membership structures that vary over the levels of the grouping factor via mmc terms. (#353)
Fit shifted log-normal models via family shifted_lognormal. (#218)
Specify nested non-linear formulas.
Introduce function make_conditions to ease preparation of conditions for marginal_effects.

Change the parameterization of weibull and exgaussian models to be consistent with other model
classes. Post-processing of related models fitted with earlier version of brms is no longer possible.
Treat integer responses in ordinal models as directly indicating categories even if the lowest integer is not one.
Improve output of the hypothesis method thanks to the ideas of Matti Vuorre. (#362)
Always plot by variables as facets in marginal_smooths.
Deprecate the cor_bsts correlation structure.

Allow the : operator to combine groups in multi-membership terms thanks to Gang Chen.
Avoid an unexpected error when calling LOO with argument reloo = TRUE thanks to Peter Konings. (#348)
Fix problems in predict when applied to categorical models thanks to Lydia Andreyevna Krasilnikova and Thomas Vladeck. (#336, #345)
Allow truncation in multivariate models with missing values thanks to Malte Lau Petersen. (#380)
Force time points to be unique within groups in autocorrelation structures thanks to Ruben Arslan. (#363)
Fix problems when post-processing multiple uncorrelated group-level terms of the same grouping factor thanks to Ivy Jansen. (#374)
Fix a problem in the Stan code of multivariate weibull and frechet models thanks to the GitHub user philj1s. (#375)
Fix a rare error when post-processing binomial models thanks to the GitHub user SeanH94. (#382)
Keep attributes of variables when preparing the model.frame thanks to Daniel Luedecke. (#393)