nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

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: 0.7.9
Imports: caret, data.table, doParallel, foreach, ggplot2, glmnet, matrixStats, matrixTests, methods, parallel, pROC, Rfast, RhpcBLASctl, rlang, ROCR
Suggests: Boruta, CORElearn, fastshap (≥ 0.1.0), gbm, ggbeeswarm, ggpubr, hsstan, mda, mlbench, pbapply, pls, randomForest, ranger, RcppEigen, rmarkdown, knitr, SuperLearner
Published: 2024-07-04
DOI: 10.32614/CRAN.package.nestedcv
Author: Myles Lewis ORCID iD [aut, cre], Athina Spiliopoulou ORCID iD [aut], Cankut Cubuk ORCID iD [ctb], Katriona Goldmann ORCID iD [ctb]
Maintainer: Myles Lewis <myles.lewis at>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-gb
Citation: nestedcv citation info
Materials: README NEWS
In views: MachineLearning
CRAN checks: nestedcv results


Reference manual: nestedcv.pdf
Vignettes: nestedcv guide
Using outercv with Bayesian shrinkage models
Explaining nestedcv models with Shapley values


Package source: nestedcv_0.7.9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): nestedcv_0.7.9.tgz, r-oldrel (arm64): nestedcv_0.7.9.tgz, r-release (x86_64): nestedcv_0.7.9.tgz, r-oldrel (x86_64): nestedcv_0.7.9.tgz
Old sources: nestedcv archive


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