studyStrap: 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) <doi:10.1101/856385>.

Package details

AuthorGabriel Loewinger [aut, cre] (<>), Giovanni Parmigiani [ths], Prasad Patil [sad], National Science Foundation Grant DMS1810829 [fnd], National Institutes of Health Grant T32 AI 007358 [fnd]
MaintainerGabriel Loewinger <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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studyStrap documentation built on Feb. 20, 2020, 5:08 p.m.