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studyStrap
View on CRAN: Click
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Download and install studyStrap package within the R console
Install from CRAN:
install.packages("studyStrap")
Install from Github:
library("remotes")
install_github("cran/studyStrap")
Install by package version:
library("remotes")
install_version("studyStrap", "1.0.0")
Attach the package and use:
library("studyStrap")
Maintained by
Gabriel Loewinger
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-02-20
Latest Update: 2020-02-20
Description:
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>.
How to cite:
Gabriel Loewinger (2020). studyStrap: Study Strap and Multi-Study Learning Algorithms. R package version 1.0.0, https://cran.r-project.org/web/packages/studyStrap
Previous versions and publish date:
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Other R packages that studyStrap depends,
imports, suggests or enhances
Functions, R codes and Examples using
the studyStrap R package
Some associated functions: cmss . fatTrim . merged . sim.metrics . ss . sse . studyStrap.predict .
Some associated R codes: SSE.caret.mSSL.R . fatTrim.R . merge.caret.mSSL.R . similarity.metrics.R . ss.ar.caret.mSSL.R . ss.caret.mSSL.R . studyStrap.predict.caret.mSSL.R . Full studyStrap package functions and examples
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