New features
- Define custom variables in all of Stan's program blocks via function
stanvar
. (#459) - Change the scope of non-linear parameters to be global within univariate models. (#390)
- Allow to automatically group predictor values in Gaussian processes specified via
gp
. This may lead to a considerable increase in sampling efficiency. (#300) - Compute LOO-adjusted R-squared using method
loo_R2
. - Compute non-linear predictors outside of a loop over observations by means of argument
loop
inbrmsformula
. - Fit non-linear mixture models. (#456)
- Fit censored or truncated mixture models. (#469)
- Allow
horseshoe
andlasso
priors to be set on special population-level effects. - Allow vectors of length greater one to be passed to
set_prior
. - Conveniently save and load fitted model objects in
brm
via argumentfile
. (#472) - Display posterior probabilities in the output of
hypothesis
.
Other changes
- Deprecate argument
stan_funs
inbrm
in favor of using thestanvars
argument for the specification of custom Stan functions. - Deprecate arguments
flist
and...
innlf
. - Deprecate argument
dpar
inlf
andnlf
.
Bug fixes
- Allow custom families in mixture models thanks to Noam Ross. (#453)
- Ensure compatibility with mice version 3.0. (#455)
- Fix naming of correlation parameters of group-level terms with multiple subgroups thanks to Kristoffer Magnusson. (#457)
- Improve scaling of default priors in
lognormal
models (#460). - Fix multiple problems in the post-processing of categorical models.
- Fix validation of nested grouping factors in post-processing methods when passing new data thanks to Liam Kendall.