mice 3.19.0

mice 3.19.0

Major changes

• Added predict_mi() to generate predictions from models fitted on
  multiply imputed datasets. The function pools predictions across
  imputations using Rubin’s rules, and can return point predictions
  or prediction intervals at a specified confidence level.

  Typical workflow:

  1. Fit a model separately on each completed dataset.
  2. Call predict_mi() with the list of models and the corresponding
     new data (per imputation).
  3. Obtain either pooled predictions (pool = TRUE) or per-imputation
     predictions (pool = FALSE).

  This functionality makes it easier to evaluate predictive performance
  on test sets while correctly accounting for imputation uncertainty.

  Contributed: @Fdvanleeuwen, @thomvolker (#720)

• Adds a correction for the Barnard-Rubin degrees of freedom calculation
  that provides stabler results for small samples and zero within-imputation
  variance. Contributed: @frederikfabriciusbjerre (#726)

Minor changes

• Adds fallback for lmer objects in pool() without requiring broom.mixed.
  Contributed: @anya-decarlo (#728)

• Explicitly load toenail data from the mice package to avoid lme4 conflict. Contributed: @bbolker (#730)