brms 1.0.0

This is one of the largest updates of brms since its initial release. In addition to many new features, the multivariate 'trait' syntax has been removed from the package as it was confusing for users, required much special case coding, and was hard to maintain. See help(brmsformula) for details of the formula syntax applied in brms.

new features

• Allow estimating correlations between
  group-level effects defined across multiple formulae
  (e.g., in non-linear models) by specifying IDs in
  each grouping term via an extended lme4 syntax.
• Implement distributional regression models
  allowing to fully predict auxiliary parameters
  of the response distribution. Among many other
  possibilities, this can be used to model
  heterogeneity of variances.
• Zero-inflated and hurdle models do not use
  multivariate syntax anymore but instead have
  special auxiliary parameters named zi and
  hu defining zero-inflation / hurdle probabilities.
• Implement the von_mises family to model
  circular responses.
• Introduce the brmsfamily function for
  convenient specification of family objects.
• Allow predictions of t2 smoothing
  terms for new data.
• Feature vectors as arguments for the addition
  argument trunc in order to model varying
  truncation points.

other changes

• Remove the cauchy family
  after several months of deprecation.
• Make sure that group-level parameter names
  are unambiguous by adding double underscores
  thanks to the idea of the GitHub user schmettow.
• The predict method now returns predicted
  probabilities instead of absolute frequencies of
  samples for ordinal and categorical models.
• Compute the linear predictor in the model
  block of the Stan program instead of in the
  transformed parameters block. This avoids saving
  samples of unnecessary parameters to disk.
  Thanks goes to Rick Arrano for pointing me
  to this issue.
• Colour points in marginal_effects plots if
  sensible.
• Set the default of the robust argument
  to TRUE in marginal_effects.brmsfit.

bug fixes

• Fix a bug that could occur when predicting
  factorial response variables for new data.
  Only affects categorical and ordinal models.
• Fix a bug that could lead to duplicated
  variable names in the Stan code when sampling
  from priors in non-linear models thanks to Tom Wallis.
• Fix problems when trying to pointwise
  evaluate non-linear formulae in
  logLik.brmsfit thanks to Tom Wallis.
• Ensure full compatibility of the ranef
  and coef methods with non-linear models.
• Fix problems that occasionally occured when
  handling dplyr datasets thanks to the
  GitHub user Atan1988.