brms 1.10.2

new features

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

bug fixes

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

brms 1.10.0

new features

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

other changes

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

bug fixes

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

brms 1.9.0

new features

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

other changes

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

bug fixes

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

brms 1.8.0

new 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 initiliaze,
  to various post-processing methods.
• Do not accidently drop the second
  dimension of matrices in summary.brmsfit
  for models with only a single observation.

brms 1.7.0

new 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
  occuring for some models with autocorrelation terms
  thanks to Markus Gesmann.
• Fix multiple problems occuring for models with
  the cor_bsts structure thanks to Andrew Ellis.

brms 1.6.1

new features

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

other changes

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

bug fixes

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

brms 1.6.0

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

brms 1.5.1

new features

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

other changes

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

bug fixes

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

brms 1.5.0

new features

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

other changes

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

bug fixes

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

brms 1.4.0

new 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.