NEWS.md

brms 2.23

New Features

• Fit extended-support Beta models via family xbeta thanks to Ioannis Kosmidis. (#1698)

Bug Fixes

• Avoid the creation of zombie workers when executing log_lik in parallel thanks to Aki Vehtari and Noa Kallioinen. For now, log_lik will use PSOCK clusters if run in parallel even on Unix systems. To avoid potential speed loss for small models, log_lik will not use option(mc.cores) anymore. These changes may be reverted once the underlying causes of this issue have been fixed. (#1658)

Other Changes

• Improve sampling efficiency of beta_binomial models. (#1703)

brms 2.22.0

New Features

• Support different Gaussian process kernels in gp terms. (#234)
• Support stratified cox models via the new addition term bhaz. (#1489)
• Support futures for parallelization in the cmdstanr backend. (#1684)
• Add method loo_epred thanks to Aki Vehtari. (#1641)
• Add priorsense support via create_priorsense_data.brmsfit thanks to Noa Kallioinen. (#1354)
• Vectorize censored log likelihoods in the Stan code when possible. (#1657)
• Force Stan to activate threading without altering the Stan code via argument force of function threading. (#1549)
• Support moment matching loo prediction methods. (#1674)

Bug Fixes

• Fix a bug that led to partially duplicated Stan code in multilevel terms thanks to Henrik Singmann. (#1651)
• Fix problems with parallel executions of post-processing functions sometimes leaving unused R instances behind. Thanks to Andrew Johnson, Aki Vehtari, and Noa Kallioinen. (#1658)
• Fix several minor bugs. (#1648, #1644, #1672, #1642, #1634, #1666, #1664)

Other Changes

• Refactor some of the internal code base to avoid evaluating many data-dependent quantities several times. (#1653)
• Smartly access internal functions when evaluating non-linear formulas. (#1635)
• Improve the documentation in several places.
• Make argument loo optional in loo_moment_match.
• Change the output format of loo_predict and loo_linpred to be more consistent with other post-processing functions.

brms 2.21.0

New Features

• Add experimental support for the pathfinder and laplace algorithms in the cmdstanr backend. (#1591)
• Automatically recompute fit criteria previously stored in the model if potentially results-changing arguments are provided to the criterion method.
• Allow to turn off automatic broadcasting of constant priors.
• Allow for joint likelihood evaluation in kfold via argument joint.
• Use several Stan built-in functions implemented since version 2.26 to improve the efficiency of multiple model classes. (#1077)

Other Changes

• Change make_stancode and make_standata to be aliases of stancode and standata, respectively. Change get_prior to be an alias of a new generic method default_prior. This enable other packages to define new stancode, standata and default_prior methods to generate Stan code and data, and extract the default priors, for their own objects building on brms. Thanks to Ven Popov for helping with this. (#1604)
• Change the default prior of the shape parameter of negbinomial models to inv_gamma(0.4, 0.3) thanks to Aki Vehtari. (#1614)
• No longer automatically canonicalize the Stan code if cmdstanr is used as backend. (#1544)
• Export read_csv_as_stanfit thanks to Ven Popov. (#1619)
• Make installation of shinystan optional. This means that the package has to be loaded, via library(shinystan), before launch_shinystan can be used. (#1595)
• Improve parameter class names in the summary output.
• Show histograms rather than densities in the plot method by default.
• Deprecate argument N in the plot method in favor of argument nvariables.
• Remove deprecated argument exact_loo in method kfold.

Bug Fixes

• Remove some remaining uses of Stan's old array syntax.
• Fix a bug in formula parsing of missing values terms with interactions thank to Guido Biele. (#1608)
• Ensure compatibility of combine_models with moment matching. (#1603)
• Ensure compatibility with the latest splines2 package version. (#1580)
• Fix output of rmulti_normal thanks to Ven Popov. (#1588)
• Prevent memory leaks when executing kfold or reloo in parallel.

brms 2.20.3

Other Changes

• Switch to the new array syntax of Stan. This increases the version requirements of Stan to >= 2.26.

brms 2.20.0

New Features

• Apply the horseshoe and R2D2 priors globally, that is, for all additive predictor terms specified in the same formula. (#1492)
• Use as.brmsprior to transform objects into a brmsprior. (#1491)
• Use matrix data as non-linear covariates. (#1488)

Other Changes

• No longer support the lasso prior as it is not a good shrinkage prior and incompatible with the newly implemented global shrinkage prior framework.
• No longer support multiple deprecated prior options for categorical and multivariate models after around 3 years of deprecation. (#1420)
• Deprecate argument newdata of get_refmodel.brmsfit(). (#1502)
• Disallow binomial models without trials argument after several years of deprecation. (#1501)

Bug Fixes

• Fix a long-standing bug in the post-processing of spline models that could lead to non-sensible results if predictions were performed on a different machine than where the model was originally fitted. Old spline models can be repaired via restructure. Special thanks to Simon Wood, Ruben Arslan, Marta Kołczyńska, Patrick Hogan, and Urs Kalbitzer. (#1465)
• Fix a bunch of minor issues occurring for rare feature combinations.

brms 2.19.0

New Features

• Model unstructured autocorrelation matrices via the unstr term thanks to the help of Sebastian Weber. (#1435)
• Model ordinal data with an extra category (non-response or similar) via the hurdle_cumulative family thanks to Stephen Wild. (#1448)
• Improve user control over model recompilation via argument recompile in post-processing methods that require a compiled Stan model.
• Extend control over the point_estimate feature in prepare_predictions via the new argument ndraws_point_estimate.
• Add support for the latent projection available in projpred versions >= 2.4.0. (#1451)

Bug Fixes

• Fix a Stan syntax error in threaded models with lasso priors. (#1427)
• Fix Stan compilation issues for some of the more special link functions such as cauchit or softplus.
• Fix a bug for predictions in projpred, previously requiring more variables in newdata than necessary. (#1457, #1459, #1460)

brms 2.18.0

New Features

• Support regression splines with fixed degrees of freedom specified via s(..., fx = TRUE).
• Reuse user-specified control arguments originally passed to the Stan backend in update and related methods. (#1373, #1378)
• Allow to retain unused factors levels via drop_unused_levels = FALSE in brm and related functions. (#1346)
• Automatically update old default priors based on new input when when updating models via update.brmsfit. (#1380)
• Allow to use dirichlet priors for more parameter types. (#1165)

Other Changes

• Improve efficiency of converting models fitted with backend = "cmdstanr" to stanfit objects thanks to Simon Mills and Jacob Socolar. (#1331)
• Allow for more O1 optimization of brms-generated Stan models thanks to Aki Vehtari. (#1382)

Bug Fixes

• Fix problems with missing boundaries of sdme parameters in models with known response standard errors thanks to Solomon Kurz. (#1348)
• Fix Stan code of gamma models with softplus link.
• Allow for more flexible data inputs to brm_multiple. (#1383)
• Ensure that control_params returns the right values for models fitted with the cmdstanr backend. (#1390)
• Fix problems in multivariate spline models when using the subset addition term. (#1385)

brms 2.17.0

New Features

• Add full user control for boundaries of most parameters via the lb and ub arguments of set_prior and related functions. (#878, #1094)
• Add family logistic_normal for simplex responses. (#1274)
• Add argument future_args to kfold and reloo for additional control over parallel execution via futures.
• Add families beta_binomial & zero_inflated_beta_binomial for potentially over-dispersed and zero-inflated binomial response models thanks to Hayden Rabel. (#1319 & #1311)
• Display ppd_* plots in pp_check via argument prefix. (#1313)
• Support the log link in binomial and beta type families. (#1316)
• Support projpred's augmented-data projection. (#1292, #1294)

Other changes

• Argument brms_seed has been added to get_refmodel.brmsfit(). (#1287)
• Deprecate argument inits in favor of init for consistency with the Stan backends.
• Improve speed of the summary method for high-dimensional models. (#1330)

Bug Fixes

• Fix Stan code of threaded multivariate models thanks to Anirban Mukherjee. (#1277)
• Fix usage of int_conditions in conditional_smooths thanks to Urs Kalbitzer. (#1280)
• Fix an error sometimes occurring for multilevel (reference) models in projpred's K-fold CV. (#1286)
• Fix response values in make_standata for bernoulli families when only 1s are present thanks to Facundo Munoz. (#1298)
• Fix pp_check for censored responses to work for all plot types thanks to Hayden Rabel. (#1327)
• Ensure that argument overwrite in add_criterion works as expected for all criteria thanks to Andrew Milne. (#1323)
• Fix a problem in launch_shinystan occurring when warmup draws were saved thanks to Frank Weber. (#1257, #1329)
• Fix numerical stability problems in log_lik for ordinal models. (#1192)

brms 2.16.3

Other changes

• Move projpred from Imports: to Suggests:. This has the important implication that users need to load or attach projpred themselves if they want to use it (the more common case is probably attaching, which is achieved by library(projpred)). (#1222)

Bug Fixes

• Ensure that argument overwrite in add_criterion is working as intended thanks to Ruben Arslan. (#1219)
• Fix a bug in get_refmodel.brmsfit() (i.e., when using projpred for a "brmsfit") causing offsets not to be recognized. (#1220)
• Several further minor bug fixes.

brms 2.16.1

Bug Fixes

• Fix a bug causing problems during post-processing of models fitted with older versions of brms and the cmdstanr backend thanks to Riccardo Fusaroli. (#1218)

brms 2.16.0

New Features

• Support several methods of the posterior package. (#1204)
• Substantially extend compatibility of brms models with emmeans thanks to Mattan S. Ben-Shachar. (#907, #1134)
• Combine missing value (mi) terms with subset addition terms. (#1063)
• Expose function get_dpar for use in the post-processing of custom families thank to Martin Modrak. (#1131)
• Support the squareplus link function in all families and distributional parameters that also allow for the log link function.
• Add argument incl_thres to posterior_linpred.brmsfit() allowing to subtract the threshold-excluding linear predictor from the thresholds in case of an ordinal family. (#1137)
• Add a "mock" backend option to facilitate testing thanks to Martin Modrak. (#1116)
• Add option file_refit = "always" to always overwrite models stored via the file argument. (#1151)
• Initial GPU support via OpenCL thanks to the help Rok Češnovar. (#1166)
• Support argument robust in method hypothesis. (#1170)
• Vectorize the Stan code of custom likelihoods via argument loop of custom_family. (#1084)
• Experimentally allow category specific effects for ordinal cumulative models. (#1060)
• Regenerate Stan code of an existing model via argument regenerate of method stancode.
• Support expose_functions for models fitted with the cmdstanr backend thanks to Sebastian Weber. (#1176)
• Support log_prob and related functionality in models fitted with the cmdstanr backend via function add_rstan_model. (#1184)

Other Changes

• Remove use of cbind to express multivariate models after over two years of deprecation (please use mvbind instead).
• Method posterior_linpred(transform = TRUE) is now equal to posterior_epred(dpar = "mu") and no longer deprecated.
• Refactor and extend internal post-processing functions for ordinal and categorical models thanks to Frank Weber. (#1159)
• Ignore NA values in interval censored boundaries as long as they are unused. (#1070)
• Take offsets into account when deriving default priors for overall intercept parameters. (#923)
• Soft deprecate measurement error (me) terms in favor of the more general and consistent missing value (mi) terms. (#698)

Bug Fixes

• Fix an issue in the post-processing of non-normal ARMA models thanks to Thomas Buehrens. (#1149)
• Fix an issue with default baseline hazard knots in cox models thanks to Malcolm Gillies. (#1143)
• Fix a bug in non-linear models caused by accidental merging of operators in the non-linear formula thanks to Fernando Miguez. (#1142)
• Correctly trigger a refit for file_refit = "on_change" if factor level names have changed thanks to Martin Modrak. (#1128)
• Validate factors in validate_newdata even when they are simultaneously used as predictors and grouping variables thanks to Martin Modrak. (#1141)
• Fix a bug in the Stan code generation of threaded mixture models with predicted mixture probabilities thanks to Riccardo Fusaroli. (#1150)
• Remove duplicated Stan code related to the horseshoe prior thanks to Max Joseph. (#1167)
• Fix an issue in the post-processing of non-looped non-linear parameters thanks to Sebastian Weber.
• Fix an issue in the Stan code of threaded non-looped non-linear models thanks to Sebastian Weber. (#1175)
• Fix problems in the post-processing of multivariate meta-analytic models that could lead to incorrect handling of known standard errors.

brms 2.15.0

New Features

• Turn off normalization in the Stan model via argument normalize. to increase sampling efficiency thanks to Andrew Johnson. (#1017, #1053)
• Enable posterior_predict for truncated continuous models even if the required CDF or quantile functions are unavailable.
• Update and export validate_prior to validate priors supplied by the user.
• Add support for within-chain threading with rstan (Stan >= 2.25) backend.
• Apply the R2-D2 shrinkage prior to population-level coefficients via function R2D2 to be used in set_prior.
• Extend support for arma correlation structures in non-normal families.
• Extend scope of variables passed via data2 for use in the evaluation of most model terms.
• Refit models previously stored on disc only when necessary thanks to Martin Modrak. The behavior can be controlled via file_refit. (#1058)
• Allow for a finer tuning of informational messages printed in brm via the silent argument. (#1076)
• Allow stanvars to alter distributional parameters. (#1061)
• Allow stanvars to be used inside threaded likelihoods. (#1111)

Other Changes

• Improve numerical stability of ordinal sequential models (families sratio and cratio) thanks to Andrew Johnson. (#1087)

Bug Fixes

• Allow fitting multinomial models with the cmdstanr backend thanks to Andrew Johnson. (#1033)
• Allow user-defined Stan functions in threaded models. (#1034)
• Allow usage of the : operator in autocorrelation terms.
• Fix Stan code generation when specifying coefficient-level priors on spline terms.
• Fix numerical issues occurring in edge cases during post-processing of Gaussian processes thanks to Marta Kołczyńska.
• Fix an error during post-processing of new levels in multi-membership terms thanks to Guilherme Mohor.
• Fix a bug in the Stan code of threaded wiener drift diffusion models thanks to the GitHub user yanivabir. (#1085)
• Fix a bug in the threaded Stan code for GPs with categorical by variables thanks to Reece Willoughby. (#1081)
• Fix a bug in the threaded Stan code when using QR decomposition thanks to Steve Bronder. (#1086)
• Include offsets in emmeans related methods thanks to Russell V. Lenth. (#1096)

brms 2.14.4

New Features

• Support projpred version 2.0 for variable selection in generalized linear and additive multilevel models thanks to Alejandro Catalina.
• Support by variables in multi-membership terms.
• Use Bayesian bootstrap in loo_R2.

Bug Fixes

• Allow non-linear terms in threaded models.
• Allow multi-membership terms in threaded models.
• Allow se addition terms in threaded models.
• Allow categorical families in threaded models.
• Fix updating of parameters in loo_moment_match.
• Fix facet labels in conditional_effects thanks to Isaac Petersen. (#1014)

brms 2.14.0

New Features

• Experimentally support within-chain parallelization via reduce_sum using argument threads in brm thanks to Sebastian Weber. (#892)
• Add algorithm fixed_param to sample from fixed parameter values. (#973)
• No longer remove NA values in data if there are unused because of the subset addition argument. (#895)
• Combine by variables and within-group correlation matrices in group-level terms. (#674)
• Add argument robust to the summary method. (#976)
• Parallelize evaluation of the posterior_predict and log_lik methods via argument cores. (#819)
• Compute effective number of parameters in kfold.
• Show prior sources and vectorization in the print output of brmsprior objects. (#761)
• Store unused variables in the model's data frame via argument unused of function brmsformula.
• Support posterior mean predictions in emmeans via dpar = "mean" thanks to Russell V. Lenth. (#993)
• Improve control of which parameters should be saved via function save_pars and corresponding argument in brm. (#746)
• Add method posterior_smooths to computing predictions of individual smooth terms. (#738)
• Allow to display grouping variables in conditional_effects using the effects argument. (#1012)

Other Changes

• Improve sampling efficiency for a lot of models by using Stan's GLM-primitives even in non-GLM cases. (#984)
• Improve sampling efficiency of multilevel models with within-group covariances thanks to David Westergaard. (#977)
• Deprecate argument probs in the conditional_effects method in favor of argument prob.

Bug Fixes

• Fix a problem in pp_check inducing wronger observation orders in time series models thanks to Fiona Seaton. (#1007)
• Fix multiple problems with loo_moment_match that prevented it from working for some more complex models.

brms 2.13.5

New Features

• Support the Cox proportional hazards model for time-to-event data via family cox. (#230, #962)
• Support method loo_moment_match, which can be used to update a loo object when Pareto k estimates are large.

Other Changes

• Improve the prediction behavior in post-processing methods when sampling new levels of grouping factors via sample_new_levels = "uncertainty". (#956)

Bug Fixes

• Fix minor problems with MKL on CRAN.

brms 2.13.3

New Features

• Fix shape parameters across multiple monotonic terms via argument id in function mo to ensure conditionally monotonic effects. (#924)
• Support package rtdists as additional backend of wiener distribution functions thanks to the help of Henrik Singmann. (#385)

Bug Fixes

• Fix generated Stan Code of models with improper global priors and constant priors on some coefficients thanks to Frank Weber. (#919)
• Fix a bug in conditional_effects occurring for categorical models with matrix predictors thanks to Jamie Cranston. (#933)

Other Changes

• Adjust behavior of the rate addition term so that it also affects the shape parameter in negbinomial models thanks to Edward Abraham. (#915)
• Adjust the default inverse-gamma prior on length-scale parameters of Gaussian processes to be less extreme in edge cases thanks to Topi Paananen.

brms 2.13.0

New Features

• Constrain ordinal thresholds to sum to zero via argument threshold in ordinal family functions thanks to the help of Marta Kołczyńska.
• Support posterior_linpred as method in conditional_effects.
• Use std_normal in the Stan code for improved efficiency.
• Add arguments cor, id, and cov to the functions gr and mm for easy specification of group-level correlation structures.
• Improve workflow to feed back brms-created models which were fitted somewhere else back into brms. (#745)
• Improve argument int_conditions in conditional_effects to work for all predictors not just interactions.
• Support multiple imputation of data passed via data2 in brm_multiple. (#886)
• Fully support the emmeans package thanks to the help of Russell V. Lenth. (#418)
• Control the within-block position of Stan code added via stanvar using the position argument.

Bug Fixes

• Fix issue in Stan code of models with multiple me terms thanks to Chris Chatham. (#855, #856)
• Fix scaling problems in the estimation of ordinal models with multiple threshold vectors thanks to Marta Kołczyńska and Rok Češnovar.
• Allow usage of std_normal in set_prior thanks to Ben Goodrich. (#867)
• Fix Stan code of distributional models with weibull, frechet, or inverse.gaussian families thanks to Brian Huey and Jack Caster. (#879)
• Fix Stan code of models which are truncated and weighted at the same time thanks to Michael Thompson. (#884)
• Fix Stan code of multivariate models with custom families and data variables passed to the likelihood thanks to Raoul Wolf. (#906)

Other Changes

• Reduce minimal scale of several default priors from 10 to 2.5. The resulting priors should remain weakly informative.
• Automatically group observations in gp for increased efficiency.
• Rename parse_bf to brmsterms and deprecate the former function.
• Rename extract_draws to prepare_predictions and deprecate the former function.
• Deprecate using a model-dependent rescor default.
• Deprecate argument cov_ranef in brm and related functions.
• Improve several internal interfaces. This should not have any user-visible changes.
• Simplify the parameterization of the horseshoe prior thanks to Aki Vehtari. (#873)
• Store fixed distributional parameters as regular draws so that they behave as if they were estimated in post-processing methods.

brms 2.12.0

New Features

• Fix parameters to constants via the prior argument. (#783)
• Specify autocorrelation terms directly in the model formula. (#708)
• Translate integer covariates in non-linear formulas to integer arrays in Stan.
• Estimate sigma in combination with fixed correlation matrices via autocorrelation term fcor.
• Use argument data2 in brm and related functions to pass data objects which cannot be passed via data. The usage of data2 will be extended in future versions.
• Compute pointwise log-likelihood values via log_lik for non-factorizable Student-t models. (#705)

Bug Fixes

• Fix output of posterior_predict for multinomial models thanks to Ivan Ukhov.
• Fix selection of group-level terms via re_formula in multivariate models thanks to Maxime Dahirel. (#834)
• Enforce correct ordering of terms in re_formula thanks to @ferberkl. (#844)
• Fix post-processing of multivariate multilevel models when multiple IDs are used for the same grouping factor thanks to @lott999. (#835)
• Store response category names of ordinal models in the output of posterior_predict again thanks to Mattew Kay. (#838)
• Handle NA values more consistently in posterior_table thanks to Anna Hake. (#845)
• Fix a bug in the Stan code of models with multiple monotonic varying effects across different groups thanks to Julian Quandt.

Other Changes

• Rename offset variables to offsets in the generated Stan code as the former will be reserved in the new stanc3 compiler.

brms 2.11.1

Bug Fixes

• Fix version requirement of the loo package.
• Fix effective sample size note in the summary output. (#824)
• Fix an edge case in the handling of covariates in special terms thanks to Andrew Milne. (#823)
• Allow restructuring objects multiple times with different brms versions thanks to Jonathan A. Nations. (#828)
• Fix validation of ordered factors in newdata thanks to Andrew Milne. (#830)

brms 2.11.0

New Features

• Support grouped ordinal threshold vectors via addition argument resp_thres. (#675)
• Support method loo_subsample for performing approximate leave-one-out cross-validation for large data.
• Allow storing more model fit criteria via add_criterion. (#793)

Bug Fixes

• Fix prediction uncertainties of new group levels for sample_new_levels = "uncertainty" thanks to Dominic Magirr. (#779)
• Fix problems when using pp_check on censored models thanks to Andrew Milne. (#744)
• Fix error in the generated Stan code of multivariate zero_inflated_binomial models thanks to Raoul Wolf. (#756)
• Fix predictions of spline models when using addition argument subset thanks to Ruben Arslan.
• Fix out-of-sample predictions of AR models when predicting more than one step ahead.
• Fix problems when using reloo or kfold with CAR models.
• Fix problems when using fitted(..., scale = "linear") with multinomial models thanks to Santiago Olivella. (#770)
• Fix problems in the as.mcmc method for thinned models thanks to @hoxo-m. (#811)
• Fix problems in parsing covariates of special effects terms thanks to Riccardo Fusaroli (#813)

Other Changes

• Rename marginal_effects to conditional_effects and marginal_smooths to conditional_smooths. (#735)
• Rename stanplot to mcmc_plot.
• Add method pp_expect as an alias of fitted. (#644)
• Model fit criteria computed via add_criterion are now stored in the brmsfit$criteria slot.
• Deprecate resp_cat in favor of resp_thres.
• Deprecate specifying global priors on regression coefficients in categorical and multivariate models.
• Improve names of weighting methods in model_weights.
• Deprecate reserved variable intercept in favor of Intercept.
• Deprecate argument exact_match in favor of fixed.
• Deprecate functions add_loo and add_waic in favor of add_criterion.

brms 2.10.0

New Features

• Improve convergence diagnostics in the summary output. (#712)
• Use primitive Stan GLM functions whenever possible. (#703)
• Pass real and integer data vectors to custom families via the addition arguments vreal and vint. (#707)
• Model compound symmetry correlations via cor_cosy. (#403)
• Predict sigma in combination with several autocorrelation structures. (#403)
• Use addition term rate to conveniently handle denominators of rate responses in log-linear models.
• Fit BYM2 CAR models via cor_car thanks to the case study and help of Mitzi Morris.

Other Changes

• Substantially improve the sampling efficiency of SAR models thanks to the GitHub user aslez. (#680)
• No longer allow changing the boundaries of autocorrelation parameters.
• Set the number of trials to 1 by default in marginal_effects if not specified otherwise. (#718)
• Use non-standard evaluation for addition terms.
• Name temporary intercept parameters more consistently in the Stan code.

Bug Fixes

• Fix problems in the post-processing of me terms with grouping factors thanks to the GitHub user tatters. (#706)
• Allow grouping variables to start with a dot thanks to Bruno Nicenboim. (#679)
• Allow the horseshoe prior in categorical and related models thanks to the Github user tatters. (#678)
• Fix extraction of prior samples for overall intercepts in prior_samples thanks to Jonas Kristoffer Lindelov. (#696)
• Allow underscores to be used in category names of categorical responses thanks to Emmanuel Charpentier. (#672)
• Fix Stan code of multivariate models with multi-membership terms thanks to the Stan discourse user Pia.
• Improve checks for non-standard variable names thanks to Ryan Holbrook. (#721)
• Fix problems when plotting facetted spaghetti plots via marginal_smooths thanks to Gavin Simpson. (#740)

brms 2.9.0

New Features

• Specify non-linear ordinal models. (#623)
• Allow to fix thresholds in ordinal mixture models (#626)
• Use the softplus link function in various families. (#622)
• Use QR decomposition of design matrices via argument decomp of brmsformula thanks to the help of Ben Goodrich. (#640)
• Define argument sparse separately for each model formula.
• Allow using bayes_R2 and loo_R2 with ordinal models. (#639)
• Support cor_arma in non-normal models. (#648)

Other Changes

• Change the parameterization of monotonic effects to improve their interpretability. (#578)
• No longer support the cor_arr and cor_bsts correlation structures after a year of deprecation.
• Refactor internal evaluation of special predictor terms.
• Improve penalty of splines thanks to Ben Goodrich and Ruben Arslan.

Bug Fixes

• Fix a problem when applying marginal_effects to measurement error models thanks to Jonathan A. Nations. (#636)
• Fix computation of log-likelihood values for weighted mixture models.
• Fix computation of fitted values for truncated lognormal and weibull models.
• Fix checking of response boundaries for models with missing values thanks to Lucas Deschamps.
• Fix Stan code of multivariate models with both residual correlations and missing value terms thanks to Solomon Kurz.
• Fix problems with interactions of special terms when extracting variable names in marginal_effects.
• Allow compiling a model in brm_multiple without sampling thanks to Will Petry. (#671)

brms 2.8.0

New Features

• Fit multinomial models via family multinomial. (#463)
• Fit Dirichlet models via family dirichlet. (#463)
• Fit conditional logistic models using the categorical and multinomial families together with non-linear formula syntax. (#560)
• Choose the reference category of categorical and related families via argument refcat of the corresponding family functions.
• Use different subsets of the data in different univariate parts of a multivariate model via addition argument subset. (#360)
• Control the centering of population-level design matrices via argument center of brmsformula and related functions.
• Add an update method for brmsfit_multiple objects. (#615)
• Split folds after group in the kfold method. (#619)

Other changes

• Deprecate compare_ic and instead recommend loo_compare for the comparison of loo objects to ensure consistency between packages. (#414)
• Use the glue package in the Stan code generation. (#549)
• Introduce mvbind to eventually replace cbind in the formula syntax of multivariate models.
• Validate several sampling-related arguments in brm before compiling the Stan model. (#576)
• Show evaluated vignettes on CRAN again. (#591)
• Export function get_y which is used to extract response values from brmsfit objects.

Bug fixes

• Fix an error when trying to change argument re_formula in bayes_R2 thanks to the GitHub user emieldl. (#592)
• Fix occasional problems when running chains in parallel via the future package thanks to Jared Knowles. (#579)
• Ensure correct ordering of response categories in ordinal models thanks to Jonas Kristoffer Lindelov. (#580)
• Ignore argument resp of marginal_effects in univariate models thanks to Vassilis Kehayas. (#589)
• Correctly disable cell-mean coding in varying effects.
• Allow to fix parameter ndt in drift diffusion models.
• Fix Stan code for t-distributed varying effects thanks to Ozgur Asar.
• Fix an error in the post-processing of monotonic effects occurring for multivariate models thanks to James Rae. (#598)
• Fix lower bounds in truncated discrete models.
• Fix checks of the original data in kfold thanks to the GitHub user gcolitti. (#602)
• Fix an error when applying the VarCorr method to meta-analytic models thanks to Michael Scharkow. (#616)

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.
• No longer sample from priors of population-level intercepts when using the default intercept parameterization.

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

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

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

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.

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 deprecated 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 originally 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)

brms 1.10.2

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 occurring for multi-membership models thanks to Hans Tierens.

brms 1.10.0

Features

• Rebuild monotonic effects from scratch to allow specifying interactions with other variables. (#239)
• Introduce methods posterior_linpred and posterior_interval for consistency with other model fitting packages based on Stan.
• Introduce function theme_black providing a black ggplot2 theme.
• Specify special group-level effects within the same terms as ordinary group-level effects.
• Add argument prob to summary, which allows to control the width of the computed uncertainty intervals. (#259)
• Add argument newdata to the kfold method.
• Add several arguments to the plot method of marginal_effects to improve control over the appearences of the plots.

Other changes

• Use the same noise-free variables for all model parts in measurement error models. (#257)
• Make names of local-level terms used in the cor_bsts structure more informative.
• Store the autocor argument within brmsformula objects.
• Store posterior and prior samples in separate slots in the output of method hypothesis.
• No longer change the default theme of ggplot2 when attaching brms. (#256)
• Make sure signs of estimates are not dropped when rounding to zero in summary.brmsfit. (#263)
• Refactor parts of extract_draws and linear_predictor to be more consistent with the rest of the package.

Bug fixes

• Do not silence the Stan parser when calling brm to get informative error messages about invalid priors.
• Fix problems with spaces in priors passed to set_prior.
• Handle non data.frame objects correctly in hypothesis.default.
• Fix a problem relating to the colour of points displayed in marginal_effects.

brms 1.9.0

Features

• Perform model comparisons based on marginal likelihoods using the methods bridge_sampler, bayes_factor, and post_prob all powered by the bridgesampling package.
• Compute a Bayesian version of R-squared with the bayes_R2 method.
• Specify non-linear models for all distributional parameters.
• Combine multiple model formulas using the + operator and the helper functions lf, nlf, and set_nl.
• Combine multiple priors using the + operator.
• Split the nlpar argument of set_prior into the three arguments resp, dpar, and nlpar to allow for more flexible prior specifications.

Other changes

• Refactor parts of the package to prepare for the implementation of more flexible multivariate models in future updates.
• Keep all constants in the log-posterior in order for bridge_sampler to be working correctly.
• Reduce the amount of renaming done within the stanfit object.
• Rename argument auxpar of fitted.brmsfit to dpar.
• Use the launch_shinystan generic provided by the shinystan package.
• Set bayesplot::theme_default() as the default ggplot2 theme when attaching brms.
• Include citations of the brms overview paper as published in the Journal of Statistical Software.

Bug fixes

• Fix problems when calling fitted with hurdle_lognormal models thanks to Meghna Krishnadas.
• Fix problems when predicting sigma in asym_laplace models thanks to Anna Josefine Sorensen.

brms 1.8.0

Features

• Fit conditional autoregressive (CAR) models via function cor_car thanks to the case study of Max Joseph.
• Fit spatial autoregressive (SAR) models via function cor_sar. Currently works for families gaussian and student.
• Implement skew normal models via family skew_normal. Thanks to Stephen Martin for suggestions on the parameterization.
• Add method reloo to perform exact cross-validation for problematic observations and kfold to perform k-fold cross-validation thanks to the Stan Team.
• Regularize non-zero coefficients in the horseshoe prior thanks to Juho Piironen and Aki Vehtari.
• Add argument new_objects to various post-processing methods to allow for passing of data objects, which cannot be passed via newdata.
• Improve parallel execution flexibility via the future package.

Other changes

• Improve efficiency and stability of ARMA models.
• Throw an error when the intercept is removed in an ordinal model instead of silently adding it back again.
• Deprecate argument threshold in brm and instead recommend passing threshold directly to the ordinal family functions.
• Throw an error instead of a message when invalid priors are passed.
• Change the default value of the autocor slot in brmsfit objects to an empty cor_brms object.
• Shorten Stan code by combining declarations and definitions where possible.

Bug fixes

• Fix problems in pp_check when the variable specified in argument x has attributes thanks to Paul Galpern.
• Fix problems when computing fitted values for truncated discrete models based on new data thanks to Nathan Doogan.
• Fix unexpected errors when passing models, which did not properly initialize, to various post-processing methods.
• Do not accidently drop the second dimension of matrices in summary.brmsfit for models with only a single observation.

brms 1.7.0

Features

• Fit latent Gaussian processes of one or more covariates via function gp specified in the model formula (#221).
• Rework methods fixef, ranef, coef, and VarCorr to be more flexible and consistent with other post-processing methods (#200).
• Generalize method hypothesis to be applicable on all objects coercible to a data.frame (#198).
• Visualize predictions via spaghetti plots using argument spaghetti in marginal_effects and marginal_smooths.
• Introduce method add_ic to store and reuse information criteria in fitted model objects (#220).
• Allow for negative weights in multi-membership grouping structures.
• Introduce an as.array method for brmsfit objects.

Other changes

• Show output of \R code in HTML vignettes thanks to Ben Goodrich (#158).
• Resolve citations in PDF vignettes thanks to Thomas Kluth (#223).
• Improve sampling efficiency for exgaussian models thanks to Alex Forrence (#222).
• Also transform data points when using argument transform in marginal_effects thanks to Markus Gesmann.

Bug fixes

• Fix an unexpected error in marginal_effects occurring for some models with autocorrelation terms thanks to Markus Gesmann.
• Fix multiple problems occurring for models with the cor_bsts structure thanks to Andrew Ellis.

brms 1.6.1

Features

• Implement zero-one-inflated beta models via family zero_one_inflated_beta.
• Allow for more link functions in zero-inflated and hurdle models.

Other changes

• Ensure full compatibility with bayesplot version 1.2.0.
• Deprecate addition argument disp.

Bug fixes

• Fix problems when setting priors on coefficients of auxiliary parameters when also setting priors on the corresponding coefficients of the mean parameter. Thanks to Matti Vuorre for reporting this bug.
• Allow ordered factors to be used as grouping variables thanks to the GitHub user itissid.

brms 1.6.0

Features

• Fit finite mixture models using family function mixture.
• Introduce method pp_mixture to compute posterior probabilities of mixture component memberships thanks to a discussion with Stephen Martin.
• Implement different ways to sample new levels of grouping factors in predict and related methods through argument sample_new_levels. Thanks to Tom Wallis and Jonah Gabry for a detailed discussion about this feature.
• Add methods loo_predict, loo_linpred, and loo_predictive_interval for computing LOO predictions thanks to Aki Vehtari and Jonah Gabry.
• Allow using offset in formulas of non-linear and auxiliary parameters.
• Allow sparse matrix multiplication in non-linear and distributional models.
• Allow using the identity link for all auxiliary parameters.
• Introduce argument negative_rt in predict and posterior_predict to distinguish responses on the upper and lower boundary in wiener diffusion models thanks to Guido Biele.
• Introduce method control_params to conveniently extract control parameters of the NUTS sampler.
• Introduce argument int_conditions in marginal_effects for enhanced plotting of two-way interactions thanks to a discussion with Thomas Kluth.
• Improve flexibility of the conditions argument of marginal_effects.
• Extend method stanplot to correctly handle some new mcmc_ plots of the bayesplot package.

Other changes

• Improve the update method to only recompile models when the Stan code changes.
• Warn about divergent transitions when calling summary or print on brmsfit objects.
• Warn about unused variables in argument conditions when calling marginal_effects.
• Export and document several distribution functions that were previously kept internal.

Bug fixes

• Fix problems with the inclusion of offsets occurring for more complicated formulas thanks to Christian Stock.
• Fix a bug that led to invalid Stan code when sampling from priors in intercept only models thanks to Tom Wallis.
• Correctly check for category specific group-level effects in non-ordinal models thanks to Wayne Folta.
• Fix problems in pp_check when specifying argument newdata together with arguments x or group.
• Rename the last column in the output of hypothesis to "star" in order to avoid problems with zero length column names thanks to the GitHub user puterleat.
• Add a missing new line statement at the end of the summary output thanks to Thomas Kluth.

brms 1.5.1

Features

• Allow horseshoe and lasso priors to be applied on population-level effects of non-linear and auxiliary parameters.
• Force recompiling Stan models in update.brmsfit via argument recompile.

Other changes

• Avoid indexing of matrices in non-linear models to slightly improve sampling speed.

Bug fixes

• Fix a severe problem (introduced in version 1.5.0), when predicting Beta models thanks to Vivian Lam.
• Fix problems when summarizing some models fitted with older version of brms thanks to Vivian Lam.
• Fix checks of argument group in method pp_check thanks to Thomas K.
• Get arguments subset and nsamples working correctly in marginal_smooths.

brms 1.5.0

Features

• Implement the generalized extreme value distribution via family gen_extreme_value.
• Improve flexibility of the horseshoe prior thanks to Juho Piironen.
• Introduce auxiliary parameter mu as an alternative to specifying effects within the formula argument in function brmsformula.
• Return fitted values of auxiliary parameters via argument auxpar of method fitted.
• Add vignette "brms_multilevel", in which the advanced formula syntax of brms is explained in detail using several examples.

Other changes

• Refactor various parts of the package to ease implementation of mixture and multivariate models in future updates. This should not have any user visible effects.
• Save the version number of rstan in element version of brmsfit objects.

Bug fixes

• Fix a rare error when predicting von_mises models thanks to John Kirwan.

brms 1.4.0

Features

• Fit quantile regression models via family asym_laplace (asymmetric Laplace distribution).
• Specify non-linear models in a (hopefully) more intuitive way using brmsformula.
• Fix auxiliary parameters to certain values through brmsformula.
• Allow family to be specified in brmsformula.
• Introduce family frechet for modelling strictly positive responses.
• Allow truncation and censoring at the same time.
• Introduce function prior_ allowing to specify priors using one-sided formulas or quote.
• Pass priors to Stan directly without performing any checks by setting check = FALSE in set_prior.
• Introduce method nsamples to extract the number of posterior samples.
• Export the main formula parsing function parse_bf.
• Add more options to customize two-dimensional surface plots created by marginal_effects or marginal_smooths.

Other changes

• Change structure of brmsformula objects to be more reliable and easier to extend.
• Make sure that parameter nu never falls below 1 to reduce convergence problems when using family student.
• Deprecate argument nonlinear.
• Deprecate family geometric.
• Rename cov_fixed to cor_fixed.
• Make handling of addition terms more transparent by exporting and documenting related functions.
• Refactor helper functions of the fitted method to be easier to extend in the future.
• Remove many units tests of internal functions and add tests of user-facing functions instead.
• Import some generics from nlme instead of lme4 to remove dependency on the latter one.
• Do not apply structure to NULL anymore to get rid of warnings in R-devel.

Bug fixes

• Fix problems when fitting smoothing terms with factors as by variables thanks to Milani Chaloupka.
• Fix a bug that could cause some monotonic effects to be ignored in the Stan code thanks to the GitHub user bschneider.
• Make sure that the data of models with only a single observation are compatible with the generated Stan code.
• Handle argument algorithm correctly in update.brmsfit.
• Fix a bug sometimes causing an error in marginal_effects when using family wiener thanks to Andrew Ellis.
• Fix problems in fitted when applied to zero_inflated_beta models thanks to Milani Chaloupka.
• Fix minor problems related to the prediction of autocorrelated models.
• Fix a few minor bugs related to the backwards compatibility of multivariate and related models fitted with brms < 1.0.0.

brms 1.3.1

Features

• Introduce the auxiliary parameter disc ('discrimination') to be used in ordinal models. By default it is not estimated but fixed to one.
• Create marginal_effects plots of two-way interactions of variables that were not explicitely modeled as interacting.

Other changes

• Move rstan to 'Imports' and Rcpp to 'Depends' in order to avoid loading rstan into the global environment automatically.

Bug fixes

• Fix a bug leading to unexpected errors in some S3 methods when applied to ordinal models.

brms 1.3.0

Features

• Fit error-in-variables models using function me in the model formulae.
• Fit multi-membership models using function mm in grouping terms.
• Add families exgaussian (exponentially modified Gaussian distribution) and wiener (Wiener diffusion model distribution) specifically suited to handle for response times.
• Add the lasso prior as an alternative to the horseshoe prior for sparse models.
• Add the methods log_posterior, nuts_params, rhat, and neff_ratio for brmsfit objects to conveniently access quantities used to diagnose sampling behavior.
• Combine chains in method as.mcmc using argument combine_chains.
• Estimate the auxiliary parameter sigma in models with known standard errors of the response by setting argument sigma to TRUE in addition function se.
• Allow visualizing two-dimensional smooths with the marginal_smooths method.

Other changes

• Require argument data to be explicitely specified in all user facing functions.
• Refactor the stanplot method to use bayesplot on the backend.
• Use the bayesplot theme as the default in all plotting functions.
• Add the abbreviations mo and cs to specify monotonic and category specific effects respectively.
• Rename generated variables in the data.frames returned by marginal_effects to avoid potential naming conflicts.
• Deprecate argument cluster and use the native cores argument of rstan instead.
• Remove argument cluster_type as it is no longer required to apply forking.
• Remove the deprecated partial argument.

brms 1.2.0

Features

• Add the new family hurdle_lognormal specifically suited for zero-inflated continuous responses.
• Introduce the pp_check method to perform various posterior predictive checks using the bayesplot package.
• Introduce the marginal_smooths method to better visualize smooth terms.
• Allow varying the scale of global shrinkage parameter of the horseshoe prior.
• Add functions prior and prior_string as aliases of set_prior, the former allowing to pass arguments without quotes "" using non-standard evaluation.
• Introduce four new vignettes explaining how to fit non-linear models, distributional models, phylogenetic models, and monotonic effects respectively.
• Extend the coef method to better handle category specific group-level effects.
• Introduce the prior_summary method for brmsfit objects to obtain a summary of prior distributions applied.
• Sample from the prior of the original population-level intercept when sample_prior = TRUE even in models with an internal temporary intercept used to improve sampling efficiency.
• Introduce methods posterior_predict, predictive_error and log_lik as (partial) aliases of predict, residuals, and logLik respectively.

Other changes

• Improve computation of Bayes factors in the hypothesis method to be less influenced by MCMC error.
• Improve documentation of default priors.
• Refactor internal structure of some formula and prior evaluating functions. This should not have any user visible effects.
• Use the bayesplot package as the new backend of plot.brmsfit.

Bug fixes

• Better mimic mgcv when parsing smooth terms to make sure all arguments are correctly handled.
• Avoid an error occurring during the prediction of new data when grouping factors with only a single factor level were supplied thanks to Tom Wallis.
• Fix marginal_effects to consistently produce plots for all covariates in non-linear models thanks to David Auty.
• Improve the update method to better recognize situations where recompliation of the Stan code is necessary thanks to Raphael P.H.
• Allow to correctly update the sample_prior argument to value "only".
• Fix an unexpected error occurring in many S3 methods when the thinning rate is not a divisor of the total number of posterior samples thanks to Paul Zerr.

brms 1.1.0

Features

• Estimate monotonic group-level effects.
• Estimate category specific group-level effects.
• Allow t2 smooth terms based on multiple covariates.
• Estimate interval censored data via the addition argument cens in the model formula.
• Allow to compute residuals also based on predicted values instead of fitted values.

Other changes

• Use the prefix bcs in parameter names of category specific effects and the prefix bm in parameter names of monotonic effects (instead of the prefix b) to simplify their identification.
• Ensure full compatibility with ggplot2 version 2.2.

Bug fixes

• Fix a bug that could result in incorrect threshold estimates for cumulative and sratio models thanks to Peter Congdon.
• Fix a bug that sometimes kept distributional gamma models from being compiled thanks to Tim Beechey.
• Fix a bug causing an error in predict and related methods when two-level factors or logical variables were used as covariates in non-linear models thanks to Martin Schmettow.
• Fix a bug causing an error when passing lists to additional arguments of smoothing functions thanks to Wayne Folta.
• Fix a bug causing an error in the prior_samples method for models with multiple group-level terms that refer to the same grouping factor thanks to Marco Tullio Liuzza.
• Fix a bug sometimes causing an error when calling marginal_effects for weighted models.

brms 1.0.1

\subsection{MINOR CHANGES

• Center design matrices inside the Stan code instead of inside make_standata.
• Get rid of several warning messages occurring on CRAN.

brms 1.0.0

This is one of the largest updates of brms since its initial release. In addition to many new features, the multivariate 'trait' syntax has been removed from the package as it was confusing for users, required much special case coding, and was hard to maintain. See help(brmsformula) for details of the formula syntax applied in brms.

Features

• Allow estimating correlations between group-level effects defined across multiple formulae (e.g., in non-linear models) by specifying IDs in each grouping term via an extended lme4 syntax.
• Implement distributional regression models allowing to fully predict auxiliary parameters of the response distribution. Among many other possibilities, this can be used to model heterogeneity of variances.
• Zero-inflated and hurdle models do not use multivariate syntax anymore but instead have special auxiliary parameters named zi and hu defining zero-inflation / hurdle probabilities.
• Implement the von_mises family to model circular responses.
• Introduce the brmsfamily function for convenient specification of family objects.
• Allow predictions of t2 smoothing terms for new data.
• Feature vectors as arguments for the addition argument trunc in order to model varying truncation points.

Other changes

• Remove the cauchy family after several months of deprecation.
• Make sure that group-level parameter names are unambiguous by adding double underscores thanks to the idea of the GitHub user schmettow.
• The predict method now returns predicted probabilities instead of absolute frequencies of samples for ordinal and categorical models.
• Compute the linear predictor in the model block of the Stan program instead of in the transformed parameters block. This avoids saving samples of unnecessary parameters to disk. Thanks goes to Rick Arrano for pointing me to this issue.
• Colour points in marginal_effects plots if sensible.
• Set the default of the robust argument to TRUE in marginal_effects.brmsfit.

Bug fixes

• Fix a bug that could occur when predicting factorial response variables for new data. Only affects categorical and ordinal models.
• Fix a bug that could lead to duplicated variable names in the Stan code when sampling from priors in non-linear models thanks to Tom Wallis.
• Fix problems when trying to pointwise evaluate non-linear formulae in logLik.brmsfit thanks to Tom Wallis.
• Ensure full compatibility of the ranef and coef methods with non-linear models.
• Fix problems that occasionally occurred when handling dplyr datasets thanks to the GitHub user Atan1988.

brms 0.10.0

Features

• Add support for generalized additive mixed models (GAMMs). Smoothing terms can be specified using the s and t2 functions in the model formula.
• Introduce as.data.frame and as.matrix methods for brmsfit objects.

Other changes

• The gaussian("log") family no longer implies a log-normal distribution, but a normal distribution with log-link to match the behavior of glm. The log-normal distribution can now be specified via family lognormal.
• Update syntax of Stan models to match the recommended syntax of Stan 2.10.

Bug fixes

• The ngrps method should now always return the correct result for non-linear models.
• Fix problems in marginal_effects for models using the reserved variable intercept thanks to Frederik Aust.
• Fix a bug in the print method of brmshypothesis objects that could lead to duplicated and thus invalid row names.
• Residual standard deviation parameters of multivariate models are again correctly displayed in the output of the summary method.
• Fix problems when using variational Bayes algorithms with brms while having rstan >= 2.10.0 installed thanks to the GitHub user cwerner87.

brms 0.9.1

Features

• Allow the '/' symbol in group-level terms in the formula argument to indicate nested grouping structures.
• Allow to compute WAIC and LOO based on the pointwise log-likelihood using argument pointwise to substantially reduce memory requirements.

Other changes

• Add horizontal lines to the errorbars in marginal_effects plots for factors.

Bug fixes

• Fix a bug that could lead to a cryptic error message when changing some parts of the model formula using the update method.
• Fix a bug that could lead to an error when calling marginal_effects for predictors that were generated with the base::scale function thanks to Tom Wallis.
• Allow interactions of numeric and categorical predictors in marginal_effects to be passed to the effects argument in any order.
• Fix a bug that could lead to incorrect results of predict and related methods when called with newdata in models using the poly function thanks to Brock Ferguson.
• Make sure that user-specified factor contrasts are always applied in multivariate models.

brms 0.9.0

Features

• Add support for monotonic effects allowing to use ordinal predictors without assuming their categories to be equidistant.
• Apply multivariate formula syntax in categorical models to considerably increase modeling flexibility.
• Add the addition argument disp to define multiplicative factors on dispersion parameters. For linear models, disp applies to the residual standard deviation sigma so that it can be used to weight observations.
• Treat the fixed effects design matrix as sparse by using the sparse argument of brm. This can considerably reduce working memory requirements if the predictors contain many zeros.
• Add the cor_fixed correlation structure to allow for fixed user-defined covariance matrices of the response variable.
• Allow to pass self-defined Stan functions via argument stan_funs of brm.
• Add the expose_functions method allowing to expose self-defined Stan functions in R.
• Extend the functionality of the update method to allow all model parts to be updated.
• Center the fixed effects design matrix also in multivariate models. This may lead to increased sampling speed in models with many predictors.

Other changes

• Refactor Stan code and data generating functions to be more consistent and easier to extent.
• Improve checks of user-define prior specifications.
• Warn about models that have not converged.
• Make sure that regression curves computed by the marginal_effects method are always smooth.
• Allow to define category specific effects in ordinal models directly within the formula argument.

Bug fixes

• Fix problems in the generated Stan code when using very long non-linear model formulas thanks to Emmanuel Charpentier.
• Fix a bug that prohibited to change priors on single standard deviation parameters in non-linear models thanks to Emmanuel Charpentier.
• Fix a bug that prohibited to use nested grouping factors in non-linear models thanks to Tom Wallis.
• Fix a bug in the linear predictor computation within R, occurring for ordinal models with multiple category specific effects. This could lead to incorrect outputs of predict, fitted, and logLik for these models.
• Make sure that the global "contrasts" option is not used when post-processing a model.

brms 0.8.0

Features

• Implement generalized non-linear models, which can be specified with the help of the nonlinear argument in brm.
• Compute and plot marginal effects using the marginal_effects method thanks to the help of Ruben Arslan.
• Implement zero-inflated beta models through family zero_inflated_beta thanks to the idea of Ali Roshan Ghias.
• Allow to restrict domain of fixed effects and autocorrelation parameters using new arguments lb and ub in function set_prior thanks to the idea of Joel Gombin.
• Add an as.mcmc method for compatibility with the coda package.
• Allow to call the WAIC, LOO, and logLik methods with new data.

Other changes

• Make sure that brms is fully compatible with loo version 0.1.5.
• Optionally define the intercept as an ordinary fixed effect to avoid the reparametrization via centering of the fixed effects design matrix.
• Do not compute the WAIC in summary by default anymore to reduce computation time of the method for larger models.
• The cauchy family is now deprecated and will be removed soon as it often has convergence issues and not much practical application anyway.
• Change the default settings of the number of chains and warmup samples to the defaults of rstan (i.e., chains = 4 and warmup = iter / 2).
• Do not remove bad behaving chains anymore as they may point to general convergence problems that are dangerous to ignore.
• Improve flexibility of the theme argument in all plotting functions.
• Only show the legend once per page, when computing trace and density plots with the plot method.
• Move code of self-defined Stan functions to inst/chunks and incorporate them into the models using rstan::stanc_builder. Also, add unit tests for these functions.

Bug fixes

• Fix problems when predicting with newdata for zero-inflated and hurdle models thanks to Ruben Arslan.
• Fix problems when predicting with newdata if it is a subset of the data stored in a brmsfit object thanks to Ruben Arslan.
• Fix data preparation for multivariate models if some responses are NA thanks to Raphael Royaute.
• Fix a bug in the predict method occurring for some multivariate models so that it now always returns the predictions of all response variables, not just the first one.
• Fix a bug in the log-likelihood computation of hurdle_poisson and hurdle_negbinomial models. This may lead to minor changes in the values obtained by WAIC and LOO for these models.
• Fix some backwards compatibility issues of models fitted with version <= 0.5.0 thanks to Ulf Koether.

brms 0.7.0

Features

• Use variational inference algorithms as alternative to the NUTS sampler by specifying argument algorithm in the brm function.
• Implement beta regression models through family Beta.
• Implement zero-inflated binomial models through family zero_inflated_binomial.
• Implement multiplicative effects for family bernoulli to fit (among others) 2PL IRT models.
• Generalize the formula argument for zero-inflated and hurdle models so that predictors can be included in only one of the two model parts thanks to the idea of Wade Blanchard.
• Combine fixed and random effects estimates using the new coef method.
• Call the residuals method with newdata thanks to the idea of Friederike Holz-Ebeling.
• Allow new levels of random effects grouping factors in the predict, fitted, and residuals methods using argument allow_new_levels.
• Selectively exclude random effects in the predict, fitted, and residuals methods using argument re_formula.
• Add a plot method for objects returned by method hypothesis to visualize prior and posterior distributions of the hypotheses being tested.

Other changes

• Improve evaluation of the response part of the formula argument to reliably allow terms with more than one variable (e.g., y/x ~ 1).
• Improve sampling efficiency of models containing many fixed effects through centering the fixed effects design matrix thanks to Wayne Folta.
• Improve sampling efficiency of models containing uncorrelated random effects specified by means of (random || group) terms in formula thanks to Ali Roshan Ghias.
• Utilize user-defined functions in the Stan code of ordinal models to improve readability as well as sampling efficiency.
• Make sure that model comparisons using LOO or WAIC are only performed when models are based on the same responses.
• Use some generic functions of the lme4 package to avoid unnecessary function masking. This leads to a change in the argument order of method VarCorr.
• Change the ggplot theme in the plot method through argument theme.
• Remove the n. prefix in arguments n.iter, n.warmup, n.thin, n.chains, and n.cluster of the brm function. The old argument names remain usable as deprecated aliases.
• Amend names of random effects parameters to simplify matching with their respective grouping factor levels.

Bug fixes

• Fix a bug in the hypothesis method that could cause valid model parameters to be falsely reported as invalid.
• Fix a bug in the prior_samples method that could cause prior samples of parameters of the same class to be artificially correlated.
• Fix Stan code of linear models with moving-average effects and non-identity link functions so that they no longer contain code related solely to autoregressive effects.
• Fix a bug in the evaluation of formula that could cause complicated random effects terms to be falsely treated as fixed effects.
• Fix several bugs when calling the fitted and predict methods with newdata thanks to Ali Roshan Ghias.

brms 0.6.0

Features

• Add support for zero-inflated and hurdle models thanks to the idea of Scott Baldwin.
• Implement inverse gaussian models through family inverse.gaussian.
• Allow to specify truncation boundaries of the response variable thanks to the idea of Maciej Beresewicz.
• Add support for autoregressive (AR) effects of residuals, which can be modeled using the cor_ar and cor_arma functions.
• Stationary autoregressive-moving-average (ARMA) effects of order one can now also be fitted using special covariance matrices.
• Implement multivariate student-t models.
• Binomial and ordinal families now support the cauchit link function.
• Allow family functions to be used in the family argument.
• Easy access to various rstan plotting functions using the stanplot method.
• Implement horseshoe priors to model sparsity in fixed effects coefficients thanks to the idea of Josh Chang.
• Automatically scale default standard deviation priors so that they remain only weakly informative independent on the response scale.
• Report model weights computed by the loo package when comparing multiple fitted models.

Other changes

• Separate the fixed effects Intercept from other fixed effects in the Stan code to slightly improve sampling efficiency.
• Move autoregressive (AR) effects of the response from the cor_ar to the cor_arr function as the result of implementing AR effects of residuals.
• Improve checks on argument newdata used in the fitted and predict method.
• Method standata is now the only way to extract data that was passed to Stan from a brmsfit object.
• Slightly improve Stan code for models containing no random effects.
• Change the default prior of the degrees of freedom of the student family to gamma(2,0.1).
• Improve readability of the output of method VarCorr.
• Export the make_stancode function to give users direct access to Stan code generated by brms.
• Rename the brmdata function to make_standata. The former remains usable as a deprecated alias.
• Improve documentation to better explain differences in autoregressive effects across R packages.

Bug fixes

• Fix a bug that could cause an unexpected error when the predict method was called with newdata.
• Avoid side effects of the rstan compilation routines that could occasionally cause R to crash.
• Make brms work correctly with loo version 0.1.3 thanks to Mauricio Garnier Villarreal and Jonah Gabry.
• Fix a bug that could cause WAIC and LOO estimates to be slightly incorrect for gaussian models with log link.

brms 0.5.0

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 the brm function, 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 problems when a factor is used as fixed effect and as random effects grouping variable at the same time thanks to Ulf Koether.
• Fix minor issues with internal parameter naming.
• Perform additional checking on user defined priors.

brms 0.4.1

Features

• Allow for sampling from all specified proper priors in the model.
• Compute Bayes-factors for point hypotheses in S3 method hypothesis.

Bug fixes

• Fix a bug that could cause an error for models with multiple grouping factors thanks to Jonathan Williams.
• Fix a bug that could cause an error for weighted poisson and exponential models.

brms 0.4.0

Features

• Implement the Watanabe-Akaike Information Criterion (WAIC).
• Implement the ||-syntax for random effects allowing for the estimation of random effects standard deviations without the estimation of correlations.
• Allow to combine multiple grouping factors within one random effects argument using the interaction symbol :.
• Generalize S3 method hypothesis to be used with all parameter classes not just fixed effects. In addition, one-sided hypothesis testing is now possible.
• Introduce new family multigaussian allowing for multivariate normal regression.
• Introduce new family bernoulli for dichotomous response variables as a more efficient alternative to families binomial or categorical in this special case.

Other changes

• Slightly change the internal structure of brms to reflect that rstan is finally on CRAN.
• Thoroughly check validity of the response variable before the data is passed to Stan.
• Prohibit variable names containing double underscores __ to avoid naming conflicts.
• Allow function calls with several arguments (e.g. poly(x,3)) in the formula argument of function brm.
• Always center random effects estimates returned by S3 method ranef around zero.
• Prevent the use of customized covariance matrices for grouping factors with multiple random effects for now.
• Remove any experimental JAGS code from the package.

Bug fixes

• Fix a bug in S3 method hypothesis leading to an error when numbers with decimal places were used in the formulation of the hypotheses.
• Fix a bug in S3 method ranef that caused an error for grouping factors with only one random effect.
• Fix a bug that could cause the fixed intercept to be wrongly estimated in the presence of multiple random intercepts thanks to Jarrod Hadfield.

brms 0.3.0

Features

• Introduce new methods parnames and posterior_samples for class 'brmsfit' to extract parameter names and posterior samples for given parameters, respectively.
• Introduce new method hypothesis for class brmsfit allowing to test non-linear hypotheses concerning fixed effects.
• Introduce new argument addition in function brm to get a more flexible approach in specifying additional information on the response variable (e.g., standard errors for meta-analysis). Alternatively, this information can also be passed to the formula argument directly.
• Introduce weighted and censored regressions through argument addition of function brm.
• Introduce new argument cov.ranef in the brm function allowing for customized covariance structures of random effects thanks to the idea of Boby Mathew.
• Introduce new argument autocor in function brm allowing for autocorrelation of the response variable.
• Introduce new functions cor.ar, cor.ma, and cor.arma, to be used with argument autocor for modeling autoregressive, moving-average, and autoregressive-moving-average models.

Other changes

• Amend parametrization of random effects to increase efficiency of the sampling algorithms.
• Improve vectorization of sampling statements.

Bug fixes

• Fix a bug that could cause an error when fitting poisson models while predict = TRUE.
• Fix a bug that caused an error when sampling only one chain while silent = TRUE.

brms 0.2.0

Features

• New S3 class brmsfit to be returned by the brm function.
• New methods for class brmsfit: summary, print, plot, predict, fixef, ranef, VarCorr, nobs, ngrps, and formula.
• Introduce new argument silent in the brm function, allowing to suppress most of Stan's intermediate output.
• Introduce new families negbinomial (negative binomial) and geometric to allow for more flexibility in modeling count data.

Other changes

• Amend warning and error messages to make them more informative.
• Correct examples in the documentation.
• Extend the README file.

Bug fixes

• Fix a bug that caused problems when formulas contained more complicated function calls.
• Fix a bug that caused an error when posterior predictives were sampled for family cumulative.
• Fix a bug that prohibited to use of improper flat priors for parameters that have proper priors by default.

brms 0.1.0

• Initial release version