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.