paul-buerkner/brms 0.9.0 on GitHub

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.

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.

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.