Brms Versions Save

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.

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.

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.

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)

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)

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)

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)

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.

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)

new features

• Allow setting priors on noise-free variables specified via function me.
• Add arguments Ksub, exact_loo and group to method kfold for defining omitted subsets according to a grouping variable or factor.
• Allow addition argument se in skew_normal models.

bug fixes

• Ensure correct behavior of horseshoe and lasso priors in multivariate models thanks to Donald Williams.
• Allow using identity links on all parameters of the wiener family thanks to Henrik Singmann. (#276)
• Use reasonable dimnames in the output of fitted when returning linear predictors of ordinal models thanks to the GitHub user atrolle. (#274)
• Fix problems in marginal_smooths occuring for multi-membership models thanks to Hans Tierens.