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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>.
Getting started
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Package details |
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| Author | Gabriel Loewinger [aut, cre] (<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] |
| Maintainer | Gabriel Loewinger <gloewinger@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.0.0 |
| Package repository | View on CRAN |
| Installation |
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