brms 1.3.1

new features

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

other changes

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

bug fixes

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

brms 1.3.0

new features

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

other changes

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

brms 1.2.0

new 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 occuring 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 occuring in many S3 methods
  when the thinning rate is not a divisor of the total
  number of posterior samples thanks to Paul Zerr.

brms 1.1.0

new 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 identifaction.
• Ensure full compatibility with
  ggplot2 version 2.2.

bug fixes

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

brms 1.0.1

minor changes

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

brms 1.0.0

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

new 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 occured when
  handling dplyr datasets thanks to the
  GitHub user Atan1988.

brms 0.10.0

new features

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

other changes

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

bug fixes

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

brms 0.9.1

new features

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

other changes

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

bug fixes

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

brms 0.9.0

new 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, occuring for ordinal models
  with multiple category specific effects. This
  could lead to incorrect outputs of predict,
  fitted, and logLik for these models.
• Make sure that the global "contrasts" option
  is not used when post-processing a model.

brms 0.8.0

new 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.