brms 2.1.0

new features

• Fit models on multiple imputed datasets via brm_multiple thanks to Ruben Arslan. (#27)
• Combine multiple brmsfit objects via function combine_models.
• Compute model averaged posterior predictions with method pp_average. (#319)
• Add new argument ordinal to marginal_effects to generate special plots for ordinal models thanks to the idea of the GitHub user silberzwiebel. (#190)
• Use informative inverse-gamma priors for length-scale parameters of Gaussian processes. (#275)
• Compute hypotheses for all levels of a grouping factor at once using argument scope in method hypothesis. (#327)
• Vectorize user-defined Stan functions exported via export_functions using argument vectorize.
• Allow predicting new data in models with ARMA autocorrelation structures.

bug fixes

• Correctly recover noise-free coefficients through me terms thanks to Ruben Arslan. As a side effect, it is no longer possible to define priors on noise-free Xme variables directly, but only on their hyper-parameters meanme and sdme.
• Fix problems in renaming parameters of the cor_bsts structure thanks to Joshua Edward Morten. (#312)
• Fix some unexpected errors when predicting from ordinal models thanks to David Hervas and Florian Bader. (#306, #307, #331)
• Fix problems when estimating and predicting multivariate ordinal models thanks to David West. (#314)
• Fix various minor problems in autocorrelation structures thanks to David West. (#320)