MUVR2: Multivariate Methods with Unbiased Variable Selection

Predictive multivariate modelling for metabolomics. Types: Classification and regression. Methods: Partial Least Squares, Random Forest ans Elastic Net Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) Variable selection: Performed internally, through tuning in the inner cross-validation loop.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats, graphics, randomForest, ranger, pROC, doParallel, foreach, caret, glmnet, splines, dplyr, psych, magrittr, mgcv, grDevices, parallel
Suggests: testthat (≥ 3.0.0)
Published: 2024-09-16
DOI: 10.32614/CRAN.package.MUVR2
Author: Carl Brunius [aut], Yingxiao Yan [aut, cre]
Maintainer: Yingxiao Yan <yingxiao at chalmers.se>
BugReports: https://github.com/MetaboComp/MUVR2/issues
License: GPL-3
URL: https://github.com/MetaboComp/MUVR2
NeedsCompilation: no
Materials: README
CRAN checks: MUVR2 results

Documentation:

Reference manual: MUVR2.pdf

Downloads:

Package source: MUVR2_0.1.0.tar.gz
Windows binaries: r-devel: MUVR2_0.1.0.zip, r-release: MUVR2_0.1.0.zip, r-oldrel: MUVR2_0.1.0.zip
macOS binaries: r-release (arm64): MUVR2_0.1.0.tgz, r-oldrel (arm64): MUVR2_0.1.0.tgz, r-release (x86_64): MUVR2_0.1.0.tgz, r-oldrel (x86_64): MUVR2_0.1.0.tgz

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