brms 0.5.0

new features

• compute the Watanabe-Akaike information criterion (WAIC) and leave-one-out cross-validation (LOO) using the loo package.
• provide an interface to shinystan with S3 method 'launch_shiny'.
• new functions 'get_prior' and 'set_prior' to make prior specifications easier.
• log-likelihood values and posterior predictive samples can now be calculated within R after the model has been fitted.
• make predictions based on new data using S3 method 'predict'.
• allow for customized covariance structures of grouping factors with multiple random effects.
• new S3 methods 'fitted' and 'residuals' to compute fitted values and residuals, respectively.

other changes

• arguments 'WAIC' and 'predict' are removed from function 'brm' as they are no longer necessary.
• new argument 'cluster_type' in function 'brm' allowing to choose the cluster type created by the parallel package
• remove chains that fail to initialize while sampling in parallel leaving the other chains untouched.
• redesign trace and density plots to be faster and more stable.
• S3 method 'VarCorr' now always returns covariance matrices regardless of whether correlations were estimated.

bug fixes

• fix a bug in S3 method 'hypothesis' related to the calculation of Bayes factors for point hypotheses.
• user defined covariance matrices that are not strictly positive definite for numerical reasons should now be handled correctly.
• fix minor issues with internal parameter naming.
• perform additional checking on user defined priors.