nestedcv: Nested Cross-Validation with 'glmnet' and 'caret' (original) (raw)
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 <doi:10.1093/bioadv/vbad048>. 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:
0.7.12
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:
2024-11-27
DOI:
10.32614/CRAN.package.nestedcv
Author:
Myles Lewis 
[aut, cre], Athina Spiliopoulou [aut], Cankut Cubuk [ctb], Katriona Goldmann [ctb]
Maintainer:
Myles Lewis <myles.lewis at qmul.ac.uk>
BugReports:
https://github.com/myles-lewis/nestedcv/issues
License:
MIT + file
URL:
https://github.com/myles-lewis/nestedcv
NeedsCompilation:
no
Language:
en-gb
Citation:
Materials:
In views:
CRAN checks: