Study Strap and Multi-Study Learning Algorithms
Overview
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.
Install
Health
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Namespaces in Imports field not imported from: ‘pls’ ‘tibble’ All declared Imports should be used.
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Documentation
- Help pages
- 7
- Examples that run
- 100%7 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 92%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing studyStrap adds 160 packages (87 with compiled code, 112 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.0.1Latest
- 1.0.02020-02-20
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-02-20
- Total releases
- 2 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.1
- Download size
- 36 KB
Cite
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