brms 0.7.0

new features

• allow to 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
• allow to combine fixed and random effects estimates using
  the new coef method
• allow to call the residuals method with newdata
• allow new levels of random effects grouping
  factors in the predict, fitted, and residuals
  methods using argument allow_new_levels
• allow to 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
• improve sampling efficiency of models containing
  uncorrelated random effects specified by means
  of (random || group) terms in formula
• 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
• allow to 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 artifically 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

brms 0.6.0

new features

• add support for zero-inflated and hurdle models
• implement inverse gaussian models through family inverse.gaussian
• allow to specify truncation boundaries of the response variable
• 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
• 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 documenation to better explain differences in
  autoregressive effects across R packages

bug fixes

• fix a bug that could cause an unexpected error
  when predict is called with new data
• 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
• fix a bug that could cause WAIC and LOO estimates to be slightly
  incorrect for gaussian models with log link

brms 0.5.0

new 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 function 'brm' 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 minor issues with internal parameter naming.
• perform additional checking on user defined priors.

brms 0.4.1

• allow for sampling from all specified proper priors in the model
• calculate Bayes factors for point hypotheses in S3 method 'hypothesis'
• fix a bug that could cause an error for models with multiple grouping factors
• fix a bug that could cause an error for weighted poisson and exponential models

brms 0.4.0

new features

• implement the Wakanabe-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.

brms 0.3.0

• introduced new methods 'par.names' and 'posterior.samples' for class 'brmsfit' to extract parameter names and posterior samples for given parameters, respectively.
• introduced new method 'hypothesis' for class 'brmsfit' allowing to test non-linear hypotheses concerning fixed effects
• introduced 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.
• introduced weighted and censored regressions through argument 'addition' of function brm
• introduced new argument 'cov.ranef' in function brm allowing for customized covariance structures of random effects
• introduced new argument 'autocor' in function brm allowing for autocorrelation of the response variable.
• introduced 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.
• amended parametrization of random effects to increase efficiency of the sampling algorithms
• improved vectorization of sampling statements
• fixed a bug that could cause an error when fitting poisson models while predict = TRUE
• fixed a bug that caused an error when sampling only one chain while silent = TRUE