paul-buerkner/brms v1.2.0 on GitHub

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.

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.

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.