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:
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