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 [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

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