MUVR2: Multivariate Methods with Unbiased Variable Selection (original) (raw)
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 |
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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 |
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:
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