# Nested Cross-Validation with 'glmnet' and 'caret'

## Documentation for package ‘nestedcv’ version 0.9.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

anova_filter | Univariate filters
barplot_var_stability | Barplot variable stability
bin_stat_filter | Univariate filter for binary classification with mixed predictor datatypes
boot_anova | Bootstrap univariate filters
boot_correl | Bootstrap univariate filters
boot_filter | Bootstrap for filter functions
boot_lm | Bootstrap univariate filters
boot_ttest | Bootstrap univariate filters
boot_wilcoxon | Bootstrap univariate filters
boruta_filter | Boruta filter
boxplot_expression | Boxplot expression levels of model predictors
class_balance | Check class balance in training folds
class_balance.default | Check class balance in training folds
class_balance.nestcv.train | Check class balance in training folds
class_stat_filter | Univariate filter for binary classification with mixed predictor datatypes
coef.cva.glmnet | Extract coefficients from a cva.glmnet object
coef.nestcv.glmnet | Extract coefficients from nestcv.glmnet object
collinear | Filter to reduce collinearity in predictors
combo_filter | Combo filter
correls2 | Correlation between a vector and a matrix
correl_filter | Univariate filters
cor_stat_filter | Univariate filter for binary classification with mixed predictor datatypes
cva.glmnet | Cross-validation of alpha for glmnet
cv_coef | Coefficients from outer CV glmnet models
cv_varImp | Extract variable importance from outer CV caret models
glmnet_coefs | glmnet coefficients
glmnet_filter | glmnet filter
hist_var_ranks | Plot variable importance rankings
innercv_preds | Inner CV predictions
innercv_preds.nestcv.glmnet | Inner CV predictions
innercv_preds.nestcv.train | Inner CV predictions
innercv_roc | Build ROC curve from left-out folds from inner CV
innercv_summary | Summarise performance on inner CV test folds
lines.prc | Add precision-recall curve to a plot
lm_filter | Linear model filter
mcc | Matthews correlation coefficient
mcc_multi | Matthews correlation coefficient
metrics | Model performance metrics
model.hsstan | hsstan model for cross-validation
nestcv.explain | Generate SHAP values from nestedcv models using shapr
nestcv.glmnet | Nested cross-validation with glmnet
nestcv.SuperLearner | Outer cross-validation of SuperLearner model
nestcv.train | Nested cross-validation for caret
one_hot | One-hot encode
outercv | Outer cross-validation of selected models
outercv.default | Outer cross-validation of selected models
outercv.formula | Outer cross-validation of selected models
plot.cva.glmnet | Plot lambda across range of alphas
plot.prc | Plot precision-recall curve
plot_alphas | Plot cross-validated glmnet alpha
plot_caret | Plot caret tuning
plot_lambdas | Plot cross-validated glmnet lambdas across outer folds
plot_shap_bar | SHAP importance bar plot
plot_shap_beeswarm | SHAP importance beeswarm plot
plot_varImp | Variable importance plot
plot_var_ranks | Plot variable importance rankings
plot_var_stability | Plot variable stability
pls_filter | Partial Least Squares filter
prc | Build precision-recall curve
prc.data.frame | Build precision-recall curve
prc.default | Build precision-recall curve
prc.nestcv.glmnet | Build precision-recall curve
prc.nestcv.SuperLearner | Build precision-recall curve
prc.nestcv.train | Build precision-recall curve
prc.outercv | Build precision-recall curve
prc.repeatcv | Build precision-recall curve
predict.cva.glmnet | Predict method for cva.glmnet models
predict.hsstan | Predict from hsstan model fitted within cross-validation
predict.nestcv.glmnet | Predict method for nestcv.glmnet fits
predSummary | Summarise prediction performance metrics
pred_nestcv_glmnet | Prediction wrappers to use shapr with nestedcv
pred_nestcv_glmnet_class | Prediction wrappers to use shapr with nestedcv
pred_SuperLearner | Prediction wrappers to use shapr with nestedcv
pred_train | Prediction wrappers to use shapr with nestedcv
pred_train_class | Prediction wrappers to use shapr with nestedcv
randomsample | Oversampling and undersampling
ranger_filter | Random forest ranger filter
relieff_filter | ReliefF filter
repeatcv | Repeated nested CV
repeatfolds | Create folds for repeated nested CV
rf_filter | Random forest filter
slim | Slim nestedcv models
smote | SMOTE
stat_filter | Univariate filter for binary classification with mixed predictor datatypes
summary_vars | Summarise variables
supervisedPCA | Supervised PCA plot
train_preds | Outer training fold predictions
train_roc | Build ROC curve from outer CV training folds
train_summary | Summarise performance on outer training folds
ttest_filter | Univariate filters
txtProgressBar2 | Text Progress Bar 2
var_direction | Variable directionality
var_stability | Variable stability
var_stability.nestcv.glmnet | Variable stability
var_stability.nestcv.train | Variable stability
var_stability.repeatcv | Variable stability
weight | Calculate weights for class imbalance
wilcoxon_filter | Univariate filters