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nestedcv
View on CRAN: Click
here
Download and install nestedcv package within the R console
Install from CRAN:
install.packages("nestedcv")
Install from Github:
library("remotes")
install_github("cran/nestedcv") Install by package version:
library("remotes")
install_version("nestedcv", "0.8.0") Attach the package and use:
library("nestedcv")
Maintained by
Myles Lewis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-07-07
Latest Update: 2025-03-10
Description:
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) . 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) .
How to cite:
Myles Lewis (2022). nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'. R package version 0.8.0, https://cran.r-project.org/web/packages/nestedcv. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:24), 0.2.3 (2022-07-07 10:40), 0.3.0 (2022-09-10 23:50), 0.4.0 (2022-10-23 18:12), 0.4.4 (2022-12-05 13:30), 0.6.1 (2023-04-16 10:20), 0.6.2 (2023-05-15 21:30), 0.6.4 (2023-05-30 12:50), 0.6.6 (2023-06-08 01:10), 0.6.7 (2023-07-02 18:10), 0.6.9 (2023-08-22 21:10), 0.7.0 (2023-10-26 19:00), 0.7.3 (2023-12-04 12:40), 0.7.4 (2024-01-30 12:40), 0.7.8 (2024-03-13 22:20), 0.7.9 (2024-07-04 17:40), 0.7.10 (2024-08-16 17:50), 0.7.12 (2024-11-27 16:50), 0.8.0 (2025-03-10 18:40), 0.8.2 (2026-04-10 09:50)
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Other R packages that nestedcv depends,
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Complete documentation for nestedcv
Functions, R codes and Examples using
the nestedcv R package
Some associated functions: anova_filter . barplot_var_stability . boot_filter . boot_ttest . boruta_filter . boxplot_expression . class_balance . coef.cva.glmnet . coef.nestcv.glmnet . collinear . combo_filter . correl_filter . correls2 . cv_coef . cv_varImp . cva.glmnet . glmnet_coefs . glmnet_filter . innercv_preds . innercv_roc . innercv_summary . layer_filter . lm_filter . model.hsstan . nestcv.SuperLearner . nestcv.glmnet . nestcv.train . one_hot . outercv . plot.cva.glmnet . plot_alphas . plot_caret . plot_lambdas . plot_shap_bar . plot_shap_beeswarm . plot_varImp . plot_var_stability . predSummary . pred_nestcv_glmnet . predict.hsstan . predict.nestcv.glmnet . randomsample . ranger_filter . relieff_filter . repeatcv . repeatfolds . rf_filter . smote . stat_filter . summary_vars . supervisedPCA . train_preds . train_roc . train_summary . ttest_filter . txtProgressBar2 . var_direction . var_stability . weight . wilcoxon_filter .
Some associated R codes: boot_filter.R . checkxy.R . class_balance.R . complex_filters.R . cv.hsstan.R . cva.glmnet.R . directionality.R . filters.R . innercv_roc.R . nest_filter_balance.R . nestcv.train.R . nestcv_SuperLearner.R . nestedcv.R . one_hot.R . outercv.R . plots.R . predSummary.R . randomsample.R . repeatcv.R . shap.R . smote.R . stat_filter.R . txtProgressBar2.R . utils.R . varImp.R . Full nestedcv package functions and examples
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