Study Strap and Multi-Study Learning Algorithms

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) .


Reference manual

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1.0.0 by Gabriel Loewinger, a year ago

Browse source code at

Authors: Gabriel Loewinger [aut, cre] , Giovanni Parmigiani [ths] , Prasad Patil [sad] , National Science Foundation Grant DMS1810829 [fnd] , National Institutes of Health Grant T32 AI 007358 [fnd]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports caret, tidyverse, pls, nnls, CCA, MatrixCorrelation, dplyr, tibble

Suggests knitr, rmarkdown

See at CRAN