studyStrap
1.0.0Study 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
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- ERROR2026-06-080 OK · 12 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
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Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 7.0 median / 38 max
- Documented parameters
- 92%
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
7 7 exported
Complexity
14.7 avg / 38 max
Call network
7 nodes / 7 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2020-02-20
- Total releases
- 1 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.1
- Download size
- 36 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet