brms 1.3.0

new features

• Fit error-in-variables models
  using function me in the model formulae.
• Fit multi-membership models using function
  mm in grouping terms.
• Add families exgaussian
  (exponentially modified Gaussian distribution)
  and wiener (Wiener diffusion model distribution)
  specifically suited to handle for response times.
• Add the lasso prior as an alternative
  to the horseshoe prior for sparse models.
• Add the methods log_posterior,
  nuts_params, rhat, and neff_ratio
  for brmsfit objects to conveniently access
  quantities used to diagnose sampling behavior.
• Combine chains in method as.mcmc using
  argument combine_chains.
• Estimate the auxiliary parameter
  sigma in models with known standard errors of
  the response by setting argument sigma to
  TRUE in addition function se.
• Allow visualizing two-dimensional smooths
  with the marginal_smooths method.

other changes

• Require argument data to be explicitely
  specified in all user facing functions.
• Refactor the stanplot method
  to use bayesplot on the backend.
• Use the bayesplot theme as the default
  in all plotting functions.
• Add the abbreviations mo and cs
  to specify monotonic and category specific effects
  respectively.
• Rename generated variables in the data.frames
  returned by marginal_effects to avoid potential
  naming conflicts.
• Deprecate argument cluster and use
  the native cores argument of rstan instead.
• Remove argument cluster_type as it is
  no longer required to apply forking.
• Remove the deprecated partial argument.