Implements multi-study learning algorithms such as
merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap,
the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights.
Embedded within the 'caret' framework, this package allows for a wide range of
single-study learners (e.g., neural networks, lasso, random forests).
The package offers over 20 default similarity measures and allows for specification of custom
similarity measures for covariate-profile similarity weighting and an accept/reject step.
This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019)