brms 1.2.0

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.

other changes

• 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.

bug fixes

• 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.