brms 2.7.0

New features

• Fit approximate and non-isotropic Gaussian processes via gp. (#540)
• Enable parallelization of model fitting in brm_multiple via the future package. (#364)
• Perform posterior predictions based on k-fold cross-validation via kfold_predict. (#468)
• Indicate observations for out-of-sample predictions in ARMA models via argument oos of extract_draws. (#539)

Other changes

• Allow factor-like variables in smooth terms. (#562)
• Make plotting of marginal_effects more robust to the usage of non-standard variable names.
• Deactivate certain data validity checks when using custom families.
• Improve efficiency of adjacent category models.
• No longer print informational messages from the Stan parser.

Bug fixes

• Fix an issue that could result in a substantial efficiency drop of various post-processing methods for larger models.
• Fix an issue when that resulted in an error when using fitted(..., scale = "linear") with ordinal models thanks to Andrew Milne. (#557)
• Allow setting priors on the overall intercept in sparse models.
• Allow sampling from models with only a single observation that also contain an offset thanks to Antonio Vargas. (#545)
• Fix an error when sampling from priors in mixture models thanks to Jacki Buros Novik. (#542)
• Fix a problem when trying to sample from priors of parameter transformations.
• Allow using marginal_smooths with ordinal models thanks to Andrew Milne. (#570)
• Fix an error in the post-processing of me terms thanks to the GitHub user hlluik. (#571)
• Correctly update warmup samples when using update.brmsfit.