brms 2.2.0

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.

other changes

• 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.

bug fixes

• 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)