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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 |
Reference manual: | studyStrap.pdf |
Vignettes: |
Introduction to studyStrap |
Package source: | studyStrap_1.0.0.tar.gz |
Windows binaries: | r-devel: studyStrap_1.0.0.zip, r-release: studyStrap_1.0.0.zip, r-oldrel: studyStrap_1.0.0.zip |
macOS binaries: | r-release (arm64): studyStrap_1.0.0.tgz, r-oldrel (arm64): studyStrap_1.0.0.tgz, r-release (x86_64): studyStrap_1.0.0.tgz, r-oldrel (x86_64): studyStrap_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.