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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