performance 0.12.1

General

• icc() and r2_nakagawa() get a null_model argument. This can be useful
  when computing R2 or ICC for mixed models, where the internal computation of
  the null model fails, or when you already have fit the null model and want
  to save time.

• icc() and r2_nakagawa() get a approximation argument indicating the
  approximation method for the distribution-specific (residual) variance. See
  Nakagawa et al. 2017 for details.

• icc() and r2_nakagawa() get a model_component argument indicating the
  component for zero-inflation or hurdle models.

• performance_rmse() (resp. rmse()) can now compute analytical and
  bootstrapped confidence intervals. The function gains following new arguments:
  ci, ci_method and iterations.

• New function r2_ferrari() to compute Ferrari & Cribari-Neto's R2 for
  generalized linear models, in particular beta-regression.

• Improved documentation of some functions.

Bug fixes

• Fixed issue in check_model() when model contained a transformed response
  variable that was named like a valid R function name (e.g., lm(log(lapply) ~ x),
  when data contained a variable named lapply).

• Fixed issue in check_predictions() for linear models when response was
  transformed as ratio (e.g. lm(succes/trials ~ x)).

• Fixed issue in r2_bayes() for mixed models from rstanarm.