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>.

Version: 1.0.0
Depends: R (≥ 3.1)
Imports: caret, tidyverse (≥ 1.2.1), pls (≥ 2.7-1), nnls (≥ 1.4), CCA (≥ 1.2), MatrixCorrelation (≥ 0.9.2), dplyr (≥ 0.8.2), tibble (≥ 2.1.3)
Suggests: knitr, rmarkdown
Published: 2020-02-20
Author: Gabriel Loewinger ORCID iD [aut, cre], Giovanni Parmigiani [ths], Prasad Patil [sad], National Science Foundation Grant DMS1810829 [fnd], National Institutes of Health Grant T32 AI 007358 [fnd]
Maintainer: Gabriel Loewinger <gloewinger at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: studyStrap results


Reference manual: studyStrap.pdf
Vignettes: Introduction to studyStrap
Package source: studyStrap_1.0.0.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: studyStrap_1.0.0.tgz, r-release: studyStrap_1.0.0.tgz, r-oldrel: studyStrap_1.0.0.tgz


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