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.

brms 2.6.0

New features

• Fit factor smooth interactions thanks to Simon Wood.
• Specify separate priors for thresholds in ordinal models. (#524)
• Pass additional arguments to rstan::stan_model via argument stan_model_args in brm. (#525)
• Save model objects via argument file in add_ic after adding model fit criteria. (#478)
• Compute density ratios based on MCMC samples via density_ratio.
• Ignore offsets in various post-processing methods via argument offset.
• Update addition terms in formulas via update_adterms.

Other changes

• Improve internal modularization of smooth terms.
• Reduce size of internal example models.

Bug fixes

• Correctly plot splines with factorial covariates via marginal_smooths.
• Allow sampling from priors in intercept only models thanks to Emmanuel Charpentier. (#529)
• Allow logical operators in non-linear formulas.

brms 2.5.0

New features

• Improve marginal_effects to better display ordinal and categorical models via argument categorical. (#491, #497)
• Improve method kfold to offer more options for specifying omitted subsets. (#510)
• Compute estimated values of non-linear parameters via argument nlpar in method fitted.
• Disable automatic cell-mean coding in model formulas without an intercept via argument cmc of brmsformula and related functions thanks to Marie Beisemann.
• Allow using the bridge_sampler method even if prior samples are drawn within the model. (#485)
• Specify post-processing functions of custom families directly in custom_family.
• Select a subset of coefficients in fixef, ranef, and coef via argument pars. (#520)
• Allow to overwrite already stored fit indices when using add_ic.

Other changes

• Ignore argument resp when post-processing univariate models thanks to Ruben Arslan. (#488)
• Deprecate argument ordinal of marginal_effects. (#491)
• Deprecate argument exact_loo of kfold. (#510)
• Deprecate usage of binomial families without specifying trials.

Bug fixes

• Correctly sample from LKJ correlation priors thanks to Donald Williams.
• Remove stored fit indices when calling update on brmsfit objects thanks to Emmanuel Charpentier. (#490)
• Fix problems when predicting a single data point using spline models thanks to Emmanuel Charpentier. (#494)
• Set Post.Prob = 1 if Evid.Ratio = Inf in method hypothesis thanks to Andrew Milne. (#509)
• Ensure correct handling of argument file in brm_multiple.

brms 2.4.0

New features

• Define custom variables in all of Stan's program blocks via function stanvar. (#459)
• Change the scope of non-linear parameters to be global within univariate models. (#390)
• Allow to automatically group predictor values in Gaussian processes specified via gp. This may lead to a considerable increase in sampling efficiency. (#300)
• Compute LOO-adjusted R-squared using method loo_R2.
• Compute non-linear predictors outside of a loop over observations by means of argument loop in brmsformula.
• Fit non-linear mixture models. (#456)
• Fit censored or truncated mixture models. (#469)
• Allow horseshoe and lasso priors to be set on special population-level effects.
• Allow vectors of length greater one to be passed to set_prior.
• Conveniently save and load fitted model objects in brm via argument file. (#472)
• Display posterior probabilities in the output of hypothesis.

Other changes

• Deprecate argument stan_funs in brm in favor of using the stanvars argument for the specification of custom Stan functions.
• Deprecate arguments flist and ... in nlf.
• Deprecate argument dpar in lf and nlf.

Bug fixes

• Allow custom families in mixture models thanks to Noam Ross. (#453)
• Ensure compatibility with mice version 3.0. (#455)
• Fix naming of correlation parameters of group-level terms with multiple subgroups thanks to Kristoffer Magnusson. (#457)
• Improve scaling of default priors in lognormal models (#460).
• Fix multiple problems in the post-processing of categorical models.
• Fix validation of nested grouping factors in post-processing methods when passing new data thanks to Liam Kendall.

brms 2.3.1

New features

• Allow censoring and truncation in zero-inflated and hurdle models. (#430)
• Export zero-inflated and hurdle distribution functions.

Other changes

• Improve sampling efficiency of the ordinal families
  cumulative, sratio, and cratio. (#433)
• Allow to specify a single k-fold subset in method kfold. (#441)

Bug fixes

• Fix a problem in launch_shinystan due to which the
  maximum treedepth was not correctly displayed thanks to
  Paul Galpern. (#431)

brms 2.3.0

Features

• Extend cor_car to support intrinsic CAR models in pairwise difference formulation thanks to the case study of Mitzi Morris.
• Compute loo and related methods for non-factorizable normal models.

Other changes

• Rename quantile columns in posterior_summary. This affects the output of predict and related methods if summary = TRUE. (#425)
• Use hashes to check if models have the same response values when performing model comparisons. (#414)
• No longer set pointwise dynamically in loo and related methods. (#416)
• No longer show information criteria in the summary output.
• Simplify internal workflow to implement native response distributions. (#421)

Bug fixes

• Allow cor_car in multivariate models with residual correlations thanks to Quentin Read. (#427)
• Fix a problem in the Stan code generation of distributional beta models thanks to Hans van Calster. (#404)
• Fix launch_shinystan.brmsfit so that all parameters are now shown correctly in the diagnose tab. (#340)

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)

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)

brms 2.0.1

new features

• Export the helper functions posterior_summary
  and posterior_table both being used to
  summarize posterior samples and predictions.

bug fixes

• Fix incorrect computation of intercepts
  in acat and cratio models
  thanks to Peter Phalen. (#302)
• Fix pointwise computation of LOO
  and WAIC in multivariate models with estimated
  residual correlation structure.
• Fix problems in various S3 methods sometimes
  requiring unused variables to be specified in
  newdata.
• Fix naming of Stan models thanks to Hao Ran Lai.

brms 2.0.0

This is the second major release of brms. The main
new feature are generalized multivariate models, which now
support everything already possible in univariate models,
but with multiple response variables. Further, the internal
structure of the package has been improved considerably to be
easier to maintain and extend in the future.
In addition, most deprecated functionality and arguments have
been removed to provide a clean new start for the package.
Models fitted with brms 1.0 or higher should remain
fully compatible with brms 2.0.

new features

• Add support for generalized multivariate models,
  where each of the univariate models may have a different
  family and autocorrelation structure.
  Residual correlations can be estimated for multivariate
  gaussian and student models. All features
  supported in univariate models are now also available in
  multivariate models. (#3)
• Specify different formulas for different
  categories in categorical models.
• Add weakly informative default priors for the
  parameter class Intercept to improve convergence
  of more complex distributional models.
• Optionally display the MC standard error in the
  summary output. (#280)
• Add argument re.form as an alias of
  re_formula to the methods posterior_predict,
  posterior_linpred, and predictive_error
  for consistency with other packages making use of
  these methods. (#283)

other changes

• Refactor many parts of the package to make
  it more consistent and easier to extend.
• Show the link functions of all
  distributional parameters in the summary
  output. (#277)
• Reduce working memory requirements when
  extracting posterior samples for use in predict
  and related methods thanks to Fanyi Zhang. (#224)
• Remove deprecated aliases of functions
  and arguments from the package. (#278)
• No longer support certain prior specifications,
  which were previously labeled as deprecated.
• Remove the depreacted addition term
  disp from the package.
• Remove old versions of methods fixef,
  ranef, coef, and VarCorr.
• No longer support models fitted with brms < 1.0,
  which used the multivariate 'trait' syntax
  orginally deprecated in brms 1.0.
• Make posterior sample extraction in the
  summary method cleaner and less error prone.
• No longer fix the seed for random number generation
  in brm to avoid unexpected behavior in simulation studies.

bug fixes

• Store stan_funs in brmsfit objects
  to allow using update on models with user-defined
  Stan functions thanks to Tom Wallis. (#288)
• Fix problems in various post-processing methods
  when applied to models with the reserved variable
  intercept in group-level terms thanks to
  the GitHub user ASKurz. (#279)
• Fix an unexpected error in predict and related
  methods when setting sample_new_levels = "gaussian"
  in models with only one group-level effect.
  Thanks to Timothy Mastny. (#286)