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 |
Documentation:
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