paul-buerkner/brms v2.14.0

brms 2.14.0

on GitHub

New Features

• Experimentally support within-chain parallelizaion via reduce_sum
  using argument threads in brm thanks to Sebastian Weber. (#892)
• Add algorithm fixed_param to sample from fixed parameter values. (#973)
• No longer remove NA values in data if there are unused because of
  the subset addition argument. (#895)
• Combine by variables and within-group correlation matrices
  in group-level terms. (#674)
• Add argument robust to the summary method. (#976)
• Parallelize evaluation of the posterior_predict and log_lik
  methods via argument cores. (#819)
• Compute effective number of parameters in kfold.
• Show prior sources and vectorization in the print output
  of brmsprior objects. (#761)
• Store unused variables in the model's data frame via
  argument unused of function brmsformula.
• Support posterior mean predictions in emmeans via
  dpar = "mean" thanks to Russell V. Lenth. (#993)
• Improve control of which parameters should be saved via
  function save_pars and corresponding argument in brm. (#746)
• Add method posterior_smooths to computing predictions
  of individual smooth terms. (#738)
• Allow to display grouping variables in conditional_effects
  using the effects argument. (#1012)

Other Changes

• Improve sampling efficiency for a lot of models by using Stan's
  GLM-primitives even in non-GLM cases. (#984)
• Improve sampling efficiency of multilevel models with
  within-group covariances thanks to David Westergaard. (#977)
• Deprecate argument probs in the conditional_effects method
  in favor of argument prob.

Bug Fixes

• Fix a problem in pp_check inducing wronger observation
  orders in time series models thanks to Fiona Seaton. (#1007)
• Fix multiple problems with loo_moment_match that prevented
  it from working for some more complex models.