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 | 
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