Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
| Version: |
0.4.4 |
| Imports: |
Boruta, caret, CORElearn, data.table, doParallel, foreach, ggplot2, glmnet, hsstan, matrixStats, matrixTests, methods, parallel, pROC, randomForest, RcppEigen, Rfast, rlang, SuperLearner |
| Suggests: |
mda, rmarkdown, knitr |
| Published: |
2022-12-05 |
| Author: |
Myles Lewis  [aut,
cre],
Athina Spiliopoulou
[aut],
Katriona Goldmann
[aut] |
| Maintainer: |
Myles Lewis <myles.lewis at qmul.ac.uk> |
| BugReports: |
https://github.com/myles-lewis/nestedcv/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/myles-lewis/nestedcv |
| NeedsCompilation: |
no |
| Language: |
en-gb |
| Materials: |
README NEWS |
| CRAN checks: |
nestedcv results |