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.
- Version1.0.0
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release02/20/2020
Documentation
Team
Gabriel Loewinger
Prasad Patil
Show author detailsRolessadGiovanni Parmigiani
Show author detailsRolesThesis advisorNational Science Foundation Grant DMS1810829
Show author detailsRolesfndNational Institutes of Health Grant T32 AI 007358
Show author detailsRolesfnd
Insights
Last 30 days
This package has been downloaded 107 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
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Last 365 days
This package has been downloaded 1,590 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 23 downloads.
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Dependencies
- Imports8 packages
- Suggests2 packages