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 DOI: 10.32614/CRAN.package.studyStrap Author: 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] Maintainer: Gabriel Loewinger <gloewinger at gmail.com> License: MIT + file LICENSE NeedsCompilation: no CRAN checks: studyStrap results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=studyStrap to link to this page.
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