Study Strap and Multi-Study Learning Algorithms R package studyStrap version 1.0.0 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>. Software 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 Gabriel Loewinger <gloewinger@gmail.com> Comprehensive R Archive Network (CRAN) Gabriel Loewinger [aut, cre] (ORCID: <https://orcid.org/0000-0002-0755-8520>), Giovanni Parmigiani [ths], Prasad Patil [sad], National Science Foundation Grant DMS1810829 [fnd], National Institutes of Health Grant T32 AI 007358 [fnd] MIT + file LICENSE (https://CRAN.R-project.org/package=studyStrap/LICENSE) 2020-02-20 application/tgz https://CRAN.R-project.org/package=studyStrap doi:10.32614/CRAN.package.studyStrap