pathfinder and laplace algorithms in the cmdstanr backend. (#1591)constant priors.kfold via argument joint.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)shape parameter of negbinomial models to inv_gamma(0.4, 0.3) thanks to Aki Vehtari. (#1614)read_csv_as_stanfit thanks to Ven Popov. (#1619)shinystan optional. This means that the package has to be loaded, via library(shinystan), before launch_shinystan can be used. (#1595)summary output.plot method by default.N in the plot method in favor of argument nvariables.exact_loo in method kfold.combine_models with moment matching. (#1603)splines2 package version. (#1580)rmulti_normal thanks to Ven Popov. (#1588)kfold or reloo in parallel.horseshoe and R2D2 priors globally, that is, for all additive predictor terms specified in the same formula. (#1492)as.brmsprior to transform objects into a brmsprior. (#1491)lasso prior as it is not a good shrinkage prior and incompatible with the newly implemented global shrinkage prior framework.newdata of get_refmodel.brmsfit(). (#1502)trials argument after several years of deprecation. (#1501)restructure. Special thanks to Simon Wood, Ruben Arslan, Marta Kołczyńska, Patrick Hogan, and Urs Kalbitzer. (#1465)unstr term thanks to the help of Sebastian Weber. (#1435)hurdle_cumulative family thanks to Stephen Wild. (#1448)recompile in post-processing methods that require a compiled Stan model.point_estimate feature in prepare_predictions via the new argument ndraws_point_estimate.lasso priors. (#1427)cauchit or softplus.newdata than necessary. (#1457, #1459, #1460)s(..., fx = TRUE).update and related methods. (#1373, #1378)drop_unused_levels = FALSE in brm and related functions. (#1346)update.brmsfit. (#1380)dirichlet priors for more parameter types. (#1165)backend = "cmdstanr" to stanfit objects thanks to Simon Mills and Jacob Socolar. (#1331)O1 optimization of brms-generated Stan models thanks to Aki Vehtari. (#1382)sdme parameters in models with known response standard errors thanks to Solomon Kurz. (#1348)gamma models with softplus link.brm_multiple. (#1383)control_params returns the right values for models fitted with the cmdstanr backend. (#1390)subset addition term. (#1385)lb and ub arguments of set_prior and related functions. (#878, #1094)logistic_normal for simplex responses. (#1274)future_args to kfold and reloo for additional control over parallel execution via futures.beta_binomial & zero_inflated_beta_binomial for potentially over-dispersed and zero-inflated binomial response models thanks to Hayden Rabel. (#1319 & #1311)ppd_* plots in pp_check via argument prefix. (#1313)log link in binomial and beta type families. (#1316)brms_seed has been added to get_refmodel.brmsfit(). (#1287)inits in favor of init for consistency with the Stan backends.summary method for high-dimensional models. (#1330)int_conditions in conditional_smooths thanks to Urs Kalbitzer. (#1280)projpred’s K-fold CV. (#1286)make_standata for bernoulli families when only 1s are present thanks to Facundo Munoz. (#1298)pp_check for censored responses to work for all plot types thanks to Hayden Rabel. (#1327)overwrite in add_criterion works as expected for all criteria thanks to Andrew Milne. (#1323)launch_shinystan occurring when warmup draws were saved thanks to Frank Weber. (#1257, #1329)log_lik for ordinal models. (#1192)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)overwrite in add_criterion is working as intended thanks to Ruben Arslan. (#1219)get_refmodel.brmsfit() (i.e., when using projpred for a "brmsfit") causing offsets not to be recognized. (#1220)cmdstanr backend thanks to Riccardo Fusaroli. (#1218)posterior package. (#1204)brms models with emmeans thanks to Mattan S. Ben-Shachar. (#907, #1134)mi) terms with subset addition terms. (#1063)get_dpar for use in the post-processing of custom families thank to Martin Modrak. (#1131)squareplus link function in all families and distributional parameters that also allow for the log link function.incl_thres to posterior_linpred.brmsfit() allowing to subtract the threshold-excluding linear predictor from the thresholds in case of an ordinal family. (#1137)"mock" backend option to facilitate testing thanks to Martin Modrak. (#1116)file_refit = "always" to always overwrite models stored via the file argument. (#1151)robust in method hypothesis. (#1170)loop of custom_family. (#1084)cumulative models. (#1060)regenerate of method stancode.expose_functions for models fitted with the cmdstanr backend thanks to Sebastian Weber. (#1176)log_prob and related functionality in models fitted with the cmdstanr backend via function add_rstan_model. (#1184)cbind to express multivariate models after over two years of deprecation (please use mvbind instead).posterior_linpred(transform = TRUE) is now equal to posterior_epred(dpar = "mu") and no longer deprecated.NA values in interval censored boundaries as long as they are unused. (#1070)me) terms in favor of the more general and consistent missing value (mi) terms. (#698)cox models thanks to Malcolm Gillies. (#1143)file_refit = "on_change" if factor level names have changed thanks to Martin Modrak. (#1128)validate_newdata even when they are simultaneously used as predictors and grouping variables thanks to Martin Modrak. (#1141)horseshoe prior thanks to Max Joseph. (#1167)normalize. to increase sampling efficiency thanks to Andrew Johnson. (#1017, #1053)posterior_predict for truncated continuous models even if the required CDF or quantile functions are unavailable.validate_prior to validate priors supplied by the user.rstan (Stan >= 2.25) backend.R2D2 to be used in set_prior.arma correlation structures in non-normal families.data2 for use in the evaluation of most model terms.file_refit. (#1058)brm via the silent argument. (#1076)stanvars to alter distributional parameters. (#1061)stanvars to be used inside threaded likelihoods. (#1111)sratio and cratio) thanks to Andrew Johnson. (#1087)multinomial models with the cmdstanr backend thanks to Andrew Johnson. (#1033): operator in autocorrelation terms.wiener drift diffusion models thanks to the GitHub user yanivabir. (#1085)by variables thanks to Reece Willoughby. (#1081)emmeans related methods thanks to Russell V. Lenth. (#1096)projpred version 2.0 for variable selection in generalized linear and additive multilevel models thanks to Alejandro Catalina.by variables in multi-membership terms.loo_R2.se addition terms in threaded models.categorical families in threaded models.loo_moment_match.conditional_effects thanks to Isaac Petersen. (#1014)reduce_sum using argument threads in brm thanks to Sebastian Weber. (#892)fixed_param to sample from fixed parameter values. (#973)NA values in data if there are unused because of the subset addition argument. (#895)by variables and within-group correlation matrices in group-level terms. (#674)robust to the summary method. (#976)posterior_predict and log_lik methods via argument cores. (#819)kfold.print output of brmsprior objects. (#761)unused of function brmsformula.emmeans via dpar = "mean" thanks to Russell V. Lenth. (#993)save_pars and corresponding argument in brm. (#746)posterior_smooths to computing predictions of individual smooth terms. (#738)conditional_effects using the effects argument. (#1012)probs in the conditional_effects method in favor of argument prob.pp_check inducing wronger observation orders in time series models thanks to Fiona Seaton. (#1007)loo_moment_match that prevented it from working for some more complex models.cox. (#230, #962)loo_moment_match, which can be used to update a loo object when Pareto k estimates are large.sample_new_levels = "uncertainty". (#956)id in function mo to ensure conditionally monotonic effects. (#924)rtdists as additional backend of wiener distribution functions thanks to the help of Henrik Singmann. (#385)constant priors on some coefficients thanks to Frank Weber. (#919)conditional_effects occurring for categorical models with matrix predictors thanks to Jamie Cranston. (#933)rate addition term so that it also affects the shape parameter in negbinomial models thanks to Edward Abraham. (#915)threshold in ordinal family functions thanks to the help of Marta Kołczyńska.posterior_linpred as method in conditional_effects.std_normal in the Stan code for improved efficiency.cor, id, and cov to the functions gr and mm for easy specification of group-level correlation structures.int_conditions in conditional_effects to work for all predictors not just interactions.data2 in brm_multiple. (#886)emmeans package thanks to the help of Russell V. Lenth. (#418)stanvar using the position argument.me terms thanks to Chris Chatham. (#855, #856)std_normal in set_prior thanks to Ben Goodrich. (#867)weibull, frechet, or inverse.gaussian families thanks to Brian Huey and Jack Caster. (#879)gp for increased efficiency.parse_bf to brmsterms and deprecate the former function.extract_draws to prepare_predictions and deprecate the former function.rescor default.cov_ranef in brm and related functions.prior argument. (#783)sigma in combination with fixed correlation matrices via autocorrelation term fcor.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.log_lik for non-factorizable Student-t models. (#705)posterior_predict for multinomial models thanks to Ivan Ukhov.re_formula in multivariate models thanks to Maxime Dahirel. (#834)re_formula thanks to @ferberkl. (#844)posterior_predict again thanks to Mattew Kay. (#838)NA values more consistently in posterior_table thanks to Anna Hake. (#845)offset variables to offsets in the generated Stan code as the former will be reserved in the new stanc3 compiler.loo package.summary output. (#824)newdata thanks to Andrew Milne. (#830)resp_thres. (#675)loo_subsample for performing approximate leave-one-out cross-validation for large data.add_criterion. (#793)sample_new_levels = "uncertainty" thanks to Dominic Magirr. (#779)pp_check on censored models thanks to Andrew Milne. (#744)zero_inflated_binomial models thanks to Raoul Wolf. (#756)subset thanks to Ruben Arslan.reloo or kfold with CAR models.fitted(..., scale = "linear") with multinomial models thanks to Santiago Olivella. (#770)as.mcmc method for thinned models thanks to @hoxo-m. (#811)marginal_effects to conditional_effects and marginal_smooths to conditional_smooths. (#735)stanplot to mcmc_plot.pp_expect as an alias of fitted. (#644)add_criterion are now stored in the brmsfit$criteria slot.resp_cat in favor of resp_thres.model_weights.intercept in favor of Intercept.exact_match in favor of fixed.add_loo and add_waic in favor of add_criterion.summary output. (#712)vreal and vint. (#707)cor_cosy. (#403)sigma in combination with several autocorrelation structures. (#403)rate to conveniently handle denominators of rate responses in log-linear models.cor_car thanks to the case study and help of Mitzi Morris.marginal_effects if not specified otherwise. (#718)me terms with grouping factors thanks to the GitHub user tatters. (#706)horseshoe prior in categorical and related models thanks to the Github user tatters. (#678)prior_samples thanks to Jonas Kristoffer Lindelov. (#696)marginal_smooths thanks to Gavin Simpson. (#740)softplus link function in various families. (#622)decomp of brmsformula thanks to the help of Ben Goodrich. (#640)sparse separately for each model formula.bayes_R2 and loo_R2 with ordinal models. (#639)cor_arma in non-normal models. (#648)cor_arr and cor_bsts correlation structures after a year of deprecation.marginal_effects to measurement error models thanks to Jonathan A. Nations. (#636)marginal_effects.brm_multiple without sampling thanks to Will Petry. (#671)multinomial. (#463)dirichlet. (#463)categorical and multinomial families together with non-linear formula syntax. (#560)categorical and related families via argument refcat of the corresponding family functions.subset. (#360)center of brmsformula and related functions.update method for brmsfit_multiple objects. (#615)group in the kfold method. (#619)compare_ic and instead recommend loo_compare for the comparison of loo objects to ensure consistency between packages. (#414)mvbind to eventually replace cbind in the formula syntax of multivariate models.brm before compiling the Stan model. (#576)get_y which is used to extract response values from brmsfit objects.re_formula in bayes_R2 thanks to the GitHub user emieldl. (#592)resp of marginal_effects in univariate models thanks to Vassilis Kehayas. (#589)ndt in drift diffusion models.kfold thanks to the GitHub user gcolitti. (#602)VarCorr method to meta-analytic models thanks to Michael Scharkow. (#616)gp. (#540)brm_multiple via the future package. (#364)kfold_predict. (#468)oos of extract_draws. (#539)marginal_effects more robust to the usage of non-standard variable names.fitted(..., scale = "linear") with ordinal models thanks to Andrew Milne. (#557)marginal_smooths with ordinal models thanks to Andrew Milne. (#570)me terms thanks to the GitHub user hlluik. (#571)warmup samples when using update.brmsfit.rstan::stan_model via argument stan_model_args in brm. (#525)file in add_ic after adding model fit criteria. (#478)density_ratio.offset.update_adterms.marginal_smooths.marginal_effects to better display ordinal and categorical models via argument categorical. (#491, #497)kfold to offer more options for specifying omitted subsets. (#510)nlpar in method fitted.cmc of brmsformula and related functions thanks to Marie Beisemann.bridge_sampler method even if prior samples are drawn within the model. (#485)custom_family.fixef, ranef, and coef via argument pars. (#520)overwrite already stored fit indices when using add_ic.resp when post-processing univariate models thanks to Ruben Arslan. (#488)ordinal of marginal_effects. (#491)exact_loo of kfold. (#510)binomial families without specifying trials.update on brmsfit objects thanks to Emmanuel Charpentier. (#490)Post.Prob = 1 if Evid.Ratio = Inf in method hypothesis thanks to Andrew Milne. (#509)file in brm_multiple.stanvar. (#459)gp. This may lead to a considerable increase in sampling efficiency. (#300)loo_R2.loop in brmsformula.horseshoe and lasso priors to be set on special population-level effects.set_prior.brm via argument file. (#472)hypothesis.stan_funs in brm in favor of using the stanvars argument for the specification of custom Stan functions.flist and ... in nlf.dpar in lf and nlf.lognormal models (#460).cumulative, sratio, and cratio. (#433)kfold. (#441)launch_shinystan due to which the maximum treedepth was not correctly displayed thanks to Paul Galpern. (#431)cor_car to support intrinsic CAR models in pairwise difference formulation thanks to the case study of Mitzi Morris.loo and related methods for non-factorizable normal models.posterior_summary. This affects the output of predict and related methods if summary = TRUE. (#425)pointwise dynamically in loo and related methods. (#416)cor_car in multivariate models with residual correlations thanks to Quentin Read. (#427)beta models thanks to Hans van Calster. (#404)launch_shinystan.brmsfit so that all parameters are now shown correctly in the diagnose tab. (#340)custom_family. (#381)mi addition term. (#27, #343)mi terms on the right-hand side of model formulas. (#27)mo, me, and mi. (#313)model_weights and loo_model_weights providing several options to compute model weights. (#268)posterior_average to extract posterior samples averaged across models. (#386)by in function gr. (#365)stanvar. (#219, #357)mmc terms. (#353)shifted_lognormal. (#218)make_conditions to ease preparation of conditions for marginal_effects.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.ordinal models as directly indicating categories even if the lowest integer is not one.hypothesis method thanks to the ideas of Matti Vuorre. (#362)by variables as facets in marginal_smooths.cor_bsts correlation structure.: operator to combine groups in multi-membership terms thanks to Gang Chen.LOO with argument reloo = TRUE thanks to Peter Konings. (#348)predict when applied to categorical models thanks to Lydia Andreyevna Krasilnikova and Thomas Vladeck. (#336, #345)weibull and frechet models thanks to the GitHub user philj1s. (#375)binomial models thanks to the GitHub user SeanH94. (#382)model.frame thanks to Daniel Luedecke. (#393)brm_multiple thanks to Ruben Arslan. (#27)brmsfit objects via function combine_models.pp_average. (#319)ordinal to marginal_effects to generate special plots for ordinal models thanks to the idea of the GitHub user silberzwiebel. (#190)scope in method hypothesis. (#327)Stan functions exported via export_functions using argument vectorize.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.cor_bsts structure thanks to Joshua Edward Morten. (#312)posterior_summary and posterior_table both being used to summarize posterior samples and predictions.acat and cratio models thanks to Peter Phalen. (#302)pointwise computation of LOO and WAIC in multivariate models with estimated residual correlation structure.newdata.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.
gaussian and student models. All features supported in univariate models are now also available in multivariate models. (#3)categorical models.Intercept to improve convergence of more complex distributional models.summary output. (#280)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)summary output. (#277)predict and related methods thanks to Fanyi Zhang. (#224)disp from the package.fixef, ranef, coef, and VarCorr.brms < 1.0, which used the multivariate 'trait' syntax originally deprecated in brms 1.0.summary method cleaner and less error prone.brm to avoid unexpected behavior in simulation studies.stan_funs in brmsfit objects to allow using update on models with user-defined Stan functions thanks to Tom Wallis. (#288)intercept in group-level terms thanks to the GitHub user ASKurz. (#279)predict and related methods when setting sample_new_levels = "gaussian" in models with only one group-level effect. Thanks to Timothy Mastny. (#286)me.Ksub, exact_loo and group to method kfold for defining omitted subsets according to a grouping variable or factor.se in skew_normal models.identity links on all parameters of the wiener family thanks to Henrik Singmann. (#276)fitted when returning linear predictors of ordinal models thanks to the GitHub user atrolle. (#274)marginal_smooths occurring for multi-membership models thanks to Hans Tierens.posterior_linpred and posterior_interval for consistency with other model fitting packages based on Stan.theme_black providing a black ggplot2 theme.prob to summary, which allows to control the width of the computed uncertainty intervals. (#259)newdata to the kfold method.plot method of marginal_effects to improve control over the appearences of the plots.cor_bsts structure more informative.autocor argument within brmsformula objects.hypothesis.ggplot2 when attaching brms. (#256)summary.brmsfit. (#263)extract_draws and linear_predictor to be more consistent with the rest of the package.Stan parser when calling brm to get informative error messages about invalid priors.set_prior.data.frame objects correctly in hypothesis.default.marginal_effects.bridge_sampler, bayes_factor, and post_prob all powered by the bridgesampling package.bayes_R2 method.+ operator and the helper functions lf, nlf, and set_nl.+ operator.nlpar argument of set_prior into the three arguments resp, dpar, and nlpar to allow for more flexible prior specifications.bridge_sampler to be working correctly.stanfit object.auxpar of fitted.brmsfit to dpar.launch_shinystan generic provided by the shinystan package.bayesplot::theme_default() as the default ggplot2 theme when attaching brms.brms overview paper as published in the Journal of Statistical Software.fitted with hurdle_lognormal models thanks to Meghna Krishnadas.sigma in asym_laplace models thanks to Anna Josefine Sorensen.cor_car thanks to the case study of Max Joseph.cor_sar. Currently works for families gaussian and student.skew_normal. Thanks to Stephen Martin for suggestions on the parameterization.reloo to perform exact cross-validation for problematic observations and kfold to perform k-fold cross-validation thanks to the Stan Team.horseshoe prior thanks to Juho Piironen and Aki Vehtari.new_objects to various post-processing methods to allow for passing of data objects, which cannot be passed via newdata.future package.threshold in brm and instead recommend passing threshold directly to the ordinal family functions.autocor slot in brmsfit objects to an empty cor_brms object.Stan code by combining declarations and definitions where possible.pp_check when the variable specified in argument x has attributes thanks to Paul Galpern.summary.brmsfit for models with only a single observation.gp specified in the model formula (#221).fixef, ranef, coef, and VarCorr to be more flexible and consistent with other post-processing methods (#200).hypothesis to be applicable on all objects coercible to a data.frame (#198).spaghetti in marginal_effects and marginal_smooths.add_ic to store and reuse information criteria in fitted model objects (#220).as.array method for brmsfit objects.exgaussian models thanks to Alex Forrence (#222).transform in marginal_effects thanks to Markus Gesmann.marginal_effects occurring for some models with autocorrelation terms thanks to Markus Gesmann.cor_bsts structure thanks to Andrew Ellis.zero_one_inflated_beta.bayesplot version 1.2.0.disp.mixture.pp_mixture to compute posterior probabilities of mixture component memberships thanks to a discussion with Stephen Martin.predict and related methods through argument sample_new_levels. Thanks to Tom Wallis and Jonah Gabry for a detailed discussion about this feature.loo_predict, loo_linpred, and loo_predictive_interval for computing LOO predictions thanks to Aki Vehtari and Jonah Gabry.offset in formulas of non-linear and auxiliary parameters.identity link for all auxiliary parameters.negative_rt in predict and posterior_predict to distinguish responses on the upper and lower boundary in wiener diffusion models thanks to Guido Biele.control_params to conveniently extract control parameters of the NUTS sampler.int_conditions in marginal_effects for enhanced plotting of two-way interactions thanks to a discussion with Thomas Kluth.conditions argument of marginal_effects.stanplot to correctly handle some new mcmc_ plots of the bayesplot package.update method to only recompile models when the Stan code changes.summary or print on brmsfit objects.conditions when calling marginal_effects.pp_check when specifying argument newdata together with arguments x or group.hypothesis to "star" in order to avoid problems with zero length column names thanks to the GitHub user puterleat.summary output thanks to Thomas Kluth.horseshoe and lasso priors to be applied on population-level effects of non-linear and auxiliary parameters.Stan models in update.brmsfit via argument recompile.Beta models thanks to Vivian Lam.brms thanks to Vivian Lam.group in method pp_check thanks to Thomas K.subset and nsamples working correctly in marginal_smooths.gen_extreme_value.horseshoe prior thanks to Juho Piironen.mu as an alternative to specifying effects within the formula argument in function brmsformula.auxpar of method fitted."brms_multilevel", in which the advanced formula syntax of brms is explained in detail using several examples.rstan in element version of brmsfit objects.von_mises models thanks to John Kirwan.asym_laplace (asymmetric Laplace distribution).brmsformula.brmsformula.family to be specified in brmsformula.frechet for modelling strictly positive responses.prior_ allowing to specify priors using one-sided formulas or quote.Stan directly without performing any checks by setting check = FALSE in set_prior.nsamples to extract the number of posterior samples.parse_bf.marginal_effects or marginal_smooths.brmsformula objects to be more reliable and easier to extend.nu never falls below 1 to reduce convergence problems when using family student.nonlinear.geometric.cov_fixed to cor_fixed.fitted method to be easier to extend in the future.nlme instead of lme4 to remove dependency on the latter one.structure to NULL anymore to get rid of warnings in R-devel.by variables thanks to Milani Chaloupka.Stan code thanks to the GitHub user bschneider.Stan code.algorithm correctly in update.brmsfit.marginal_effects when using family wiener thanks to Andrew Ellis.fitted when applied to zero_inflated_beta models thanks to Milani Chaloupka.brms < 1.0.0.disc (‘discrimination’) to be used in ordinal models. By default it is not estimated but fixed to one.marginal_effects plots of two-way interactions of variables that were not explicitely modeled as interacting.rstan to ‘Imports’ and Rcpp to ‘Depends’ in order to avoid loading rstan into the global environment automatically.me in the model formulae.mm in grouping terms.exgaussian (exponentially modified Gaussian distribution) and wiener (Wiener diffusion model distribution) specifically suited to handle for response times.lasso prior as an alternative to the horseshoe prior for sparse models.log_posterior, nuts_params, rhat, and neff_ratio for brmsfit objects to conveniently access quantities used to diagnose sampling behavior.as.mcmc using argument combine_chains.sigma in models with known standard errors of the response by setting argument sigma to TRUE in addition function se.marginal_smooths method.data to be explicitely specified in all user facing functions.stanplot method to use bayesplot on the backend.bayesplot theme as the default in all plotting functions.mo and cs to specify monotonic and category specific effects respectively.marginal_effects to avoid potential naming conflicts.cluster and use the native cores argument of rstan instead.cluster_type as it is no longer required to apply forking.partial argument.hurdle_lognormal specifically suited for zero-inflated continuous responses.pp_check method to perform various posterior predictive checks using the bayesplot package.marginal_smooths method to better visualize smooth terms.horseshoe prior.prior and prior_string as aliases of set_prior, the former allowing to pass arguments without quotes "" using non-standard evaluation.coef method to better handle category specific group-level effects.prior_summary method for brmsfit objects to obtain a summary of prior distributions applied.sample_prior = TRUE even in models with an internal temporary intercept used to improve sampling efficiency.posterior_predict, predictive_error and log_lik as (partial) aliases of predict, residuals, and logLik respectively.hypothesis method to be less influenced by MCMC error.bayesplot package as the new backend of plot.brmsfit.mgcv when parsing smooth terms to make sure all arguments are correctly handled.marginal_effects to consistently produce plots for all covariates in non-linear models thanks to David Auty.update method to better recognize situations where recompliation of the Stan code is necessary thanks to Raphael P.H.update the sample_prior argument to value "only".t2 smooth terms based on multiple covariates.cens in the model formula.residuals also based on predicted values instead of fitted values.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.ggplot2 version 2.2.cumulative and sratio models thanks to Peter Congdon.gamma models from being compiled thanks to Tim Beechey.predict and related methods when two-level factors or logical variables were used as covariates in non-linear models thanks to Martin Schmettow.prior_samples method for models with multiple group-level terms that refer to the same grouping factor thanks to Marco Tullio Liuzza.marginal_effects for weighted models.\subsection{MINOR CHANGES
make_standata.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.
lme4 syntax.zi and hu defining zero-inflation / hurdle probabilities.von_mises family to model circular responses.brmsfamily function for convenient specification of family objects.t2 smoothing terms for new data.trunc in order to model varying truncation points.cauchy family after several months of deprecation.predict method now returns predicted probabilities instead of absolute frequencies of samples for ordinal and categorical models.marginal_effects plots if sensible.robust argument to TRUE in marginal_effects.brmsfit.logLik.brmsfit thanks to Tom Wallis.ranef and coef methods with non-linear models.dplyr datasets thanks to the GitHub user Atan1988.s and t2 functions in the model formula.as.data.frame and as.matrix methods for brmsfit objects.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.Stan models to match the recommended syntax of Stan 2.10.ngrps method should now always return the correct result for non-linear models.marginal_effects for models using the reserved variable intercept thanks to Frederik Aust.print method of brmshypothesis objects that could lead to duplicated and thus invalid row names.summary method.brms while having rstan >= 2.10.0 installed thanks to the GitHub user cwerner87.formula argument to indicate nested grouping structures.WAIC and LOO based on the pointwise log-likelihood using argument pointwise to substantially reduce memory requirements.marginal_effects plots for factors.formula using the update method.marginal_effects for predictors that were generated with the base::scale function thanks to Tom Wallis.marginal_effects to be passed to the effects argument in any order.predict and related methods when called with newdata in models using the poly function thanks to Brock Ferguson.monotonic effects allowing to use ordinal predictors without assuming their categories to be equidistant.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.sparse argument of brm. This can considerably reduce working memory requirements if the predictors contain many zeros.cor_fixed correlation structure to allow for fixed user-defined covariance matrices of the response variable.Stan functions via argument stan_funs of brm.expose_functions method allowing to expose self-defined Stan functions in R.update method to allow all model parts to be updated.Stan code and data generating functions to be more consistent and easier to extent.marginal_effects method are always smooth.formula argument.Stan code when using very long non-linear model formulas thanks to Emmanuel Charpentier.R, occurring for ordinal models with multiple category specific effects. This could lead to incorrect outputs of predict, fitted, and logLik for these models."contrasts" option is not used when post-processing a model.nonlinear argument in brm.marginal_effects method thanks to the help of Ruben Arslan.zero_inflated_beta thanks to the idea of Ali Roshan Ghias.lb and ub in function set_prior thanks to the idea of Joel Gombin.as.mcmc method for compatibility with the coda package.WAIC, LOO, and logLik methods with new data.brms is fully compatible with loo version 0.1.5.summary by default anymore to reduce computation time of the method for larger models.cauchy family is now deprecated and will be removed soon as it often has convergence issues and not much practical application anyway.rstan (i.e., chains = 4 and warmup = iter / 2).theme argument in all plotting functions.plot method.Stan functions to inst/chunks and incorporate them into the models using rstan::stanc_builder. Also, add unit tests for these functions.newdata for zero-inflated and hurdle models thanks to Ruben Arslan.newdata if it is a subset of the data stored in a brmsfit object thanks to Ruben Arslan.NA thanks to Raphael Royaute.predict method occurring for some multivariate models so that it now always returns the predictions of all response variables, not just the first one.hurdle_poisson and hurdle_negbinomial models. This may lead to minor changes in the values obtained by WAIC and LOO for these models.algorithm in the brm function.Beta.zero_inflated_binomial.bernoulli to fit (among others) 2PL IRT models.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.coef method.residuals method with newdata thanks to the idea of Friederike Holz-Ebeling.predict, fitted, and residuals methods using argument allow_new_levels.predict, fitted, and residuals methods using argument re_formula.plot method for objects returned by method hypothesis to visualize prior and posterior distributions of the hypotheses being tested.formula argument to reliably allow terms with more than one variable (e.g., y/x ~ 1).(random || group) terms in formula thanks to Ali Roshan Ghias.Stan code of ordinal models to improve readability as well as sampling efficiency.LOO or WAIC are only performed when models are based on the same responses.lme4 package to avoid unnecessary function masking. This leads to a change in the argument order of method VarCorr.ggplot theme in the plot method through argument theme.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.hypothesis method that could cause valid model parameters to be falsely reported as invalid.prior_samples method that could cause prior samples of parameters of the same class to be artificially correlated.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.formula that could cause complicated random effects terms to be falsely treated as fixed effects.fitted and predict methods with newdata thanks to Ali Roshan Ghias.inverse.gaussian.cor_ar and cor_arma functions.cauchit link function.family argument.rstan plotting functions using the stanplot method.loo package when comparing multiple fitted models.Stan code to slightly improve sampling efficiency.cor_ar to the cor_arr function as the result of implementing AR effects of residuals.newdata used in the fitted and predict method.standata is now the only way to extract data that was passed to Stan from a brmsfit object.Stan code for models containing no random effects.student family to gamma(2,0.1).VarCorr.make_stancode function to give users direct access to Stan code generated by brms.brmdata function to make_standata. The former remains usable as a deprecated alias.predict method was called with newdata.rstan compilation routines that could occasionally cause R to crash.brms work correctly with loo version 0.1.3 thanks to Mauricio Garnier Villarreal and Jonah Gabry.gaussian models with log link.loo package.shinystan with S3 method launch_shiny.get_prior and set_prior to make prior specifications easier.predict.fitted and residuals to compute fitted values and residuals, respectively.WAIC and predict are removed from the brm function, as they are no longer necessary.cluster_type in function brm allowing to choose the cluster type created by the parallel package.VarCorr now always returns covariance matrices regardless of whether correlations were estimated.hypothesis related to the calculation of Bayes-factors for point hypotheses.hypothesis.||-syntax for random effects allowing for the estimation of random effects standard deviations without the estimation of correlations.:.hypothesis to be used with all parameter classes not just fixed effects. In addition, one-sided hypothesis testing is now possible.multigaussian allowing for multivariate normal regression.bernoulli for dichotomous response variables as a more efficient alternative to families binomial or categorical in this special case.rstan is finally on CRAN.Stan.__ to avoid naming conflicts.poly(x,3)) in the formula argument of function brm.ranef around zero.JAGS code from the package.hypothesis leading to an error when numbers with decimal places were used in the formulation of the hypotheses.ranef that caused an error for grouping factors with only one random effect.parnames and posterior_samples for class ‘brmsfit’ to extract parameter names and posterior samples for given parameters, respectively.hypothesis for class brmsfit allowing to test non-linear hypotheses concerning fixed effects.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.addition of function brm.cov.ranef in the brm function allowing for customized covariance structures of random effects thanks to the idea of Boby Mathew.autocor in function brm allowing for autocorrelation of the response variable.cor.ar, cor.ma, and cor.arma, to be used with argument autocor for modeling autoregressive, moving-average, and autoregressive-moving-average models.predict = TRUE.silent = TRUE.brmsfit to be returned by the brm function.brmsfit: summary, print, plot, predict, fixef, ranef, VarCorr, nobs, ngrps, and formula.silent in the brm function, allowing to suppress most of Stan’s intermediate output.negbinomial (negative binomial) and geometric to allow for more flexibility in modeling count data.cumulative.