                 Changes in version 2.19.0 (2023-03-14)                 

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)

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 occuring for rare feature combinations.

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)

                 Changes in version 2.18.0 (2022-09-19)                 

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)

                 Changes in version 2.17.0 (2022-04-13)                 

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)

                 Changes in version 2.16.3 (2021-11-22)                 

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.

                 Changes in version 2.16.1 (2021-08-23)                 

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)

                 Changes in version 2.16.0 (2021-08-18)                 

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.

                 Changes in version 2.15.0 (2021-03-14)                 

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)

                 Changes in version 2.14.4 (2020-11-03)                 

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)

                 Changes in version 2.14.0 (2020-10-08)                 

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.

                 Changes in version 2.13.5 (2020-07-31)                 

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.

                 Changes in version 2.13.3 (2020-07-13)                 

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.

                 Changes in version 2.13.0 (2020-05-27)                 

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.

                 Changes in version 2.12.0 (2020-02-23)                 

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.

                 Changes in version 2.11.1 (2020-01-19)                 

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)

                 Changes in version 2.11.0 (2020-01-12)                 

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.

                 Changes in version 2.10.0 (2019-08-29)                 

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)

                 Changes in version 2.9.0 (2019-05-23)                  

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)

                 Changes in version 2.8.0 (2019-03-15)                  

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)

                 Changes in version 2.7.0 (2018-12-17)                  

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.

                 Changes in version 2.6.0 (2018-10-23)                  

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.

                 Changes in version 2.5.0 (2018-09-16)                  

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.

                 Changes in version 2.4.0 (2018-07-20)                  

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.

                 Changes in version 2.3.1 (2018-06-05)                  

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)

                 Changes in version 2.3.0 (2018-05-14)                  

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)

                 Changes in version 2.2.0 (2018-04-13)                  

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)

                 Changes in version 2.1.0 (2018-01-23)                  

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)

                 Changes in version 2.0.1 (2017-12-21)                  

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.

                 Changes in version 2.0.0 (2017-12-15)                  

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)

                 Changes in version 1.10.2 (2017-10-20)                 

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.

                 Changes in version 1.10.0 (2017-09-09)                 

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.

                 Changes in version 1.9.0 (2017-08-15)                  

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.

                 Changes in version 1.8.0 (2017-07-20)                  

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.

                 Changes in version 1.7.0 (2017-05-23)                  

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.

                 Changes in version 1.6.1 (2017-04-17)                  

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.

                 Changes in version 1.6.0 (2017-04-06)                  

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.

                 Changes in version 1.5.1 (2017-02-26)                  

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.

                 Changes in version 1.5.0 (2017-02-17)                  

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.

                 Changes in version 1.4.0 (2017-01-27)                  

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.

                 Changes in version 1.3.1 (2016-12-21)                  

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.

                 Changes in version 1.3.0 (2016-12-19)                  

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.

                 Changes in version 1.2.0 (2016-11-22)                  

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.

                 Changes in version 1.1.0 (2016-10-11)                  

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.

                 Changes in version 1.0.1 (2016-09-16)                  

\subsection{MINOR CHANGES

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

                 Changes in version 1.0.0 (2016-09-15)                  

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.

                 Changes in version 0.10.0 (2016-06-29)                 

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.

                 Changes in version 0.9.1 (2016-05-17)                  

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.

                 Changes in version 0.9.0 (2016-04-19)                  

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.

                 Changes in version 0.8.0 (2016-02-15)                  

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.

                 Changes in version 0.7.0 (2016-01-18)                  

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.

                 Changes in version 0.6.0 (2015-11-14)                  

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.

                 Changes in version 0.5.0 (2015-09-13)                  

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.

                 Changes in version 0.4.1 (2015-08-03)                  

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.

                 Changes in version 0.4.0 (2015-07-23)                  

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.

                 Changes in version 0.3.0 (2015-06-29)                  

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.

                 Changes in version 0.2.0 (2015-05-27)                  

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.

                 Changes in version 0.1.0 (2015-05-08)                  

  - Initial release version
