Study Strap and Multi-Study Learning Algorithms
Covariate-Matched Study Strap for Multi-Study Learning: Fits accept/re...
fatTrim: Supporting function to reduce the size of models
Merged Approach for Multi-Study Learning: fits a single model on all s...
Study Strap similarity measures: Supporting function used as the defau...
The Study Strap for Multi-Study Learning: Fits Study Strap algorithm
Trained-on-Observed-Studies Ensemble (Study-Specific Ensemble) for Mul...
Study Strap Prediction Function: Makes predictions on object of class ...
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) <doi:10.1101/856385>.