Nested Cross-Validation with 'glmnet' and 'caret'
Plot cross-validated glmnet lambdas across outer folds
SHAP importance bar plot
Extract variable importance from outer CV caret models
Coefficients from outer CV glmnet models
Linear model filter
Plot caret tuning
Create folds for repeated nested CV
Barplot variable stability
Bootstrap for filter functions
Bootstrap univariate filters
Boruta filter
Boxplot expression levels of model predictors
Check class balance in training folds
Extract coefficients from a cva.glmnet object
Extract coefficients from nestcv.glmnet object
Filter to reduce collinearity in predictors
Combo filter
Correlation between a vector and a matrix
Cross-validation of alpha for glmnet
glmnet coefficients
glmnet filter
Inner CV predictions
Build ROC curve from left-out folds from inner CV
Summarise performance on inner CV test folds
Add precision-recall curve to a plot
Matthews correlation coefficient
Model performance metrics
hsstan model for cross-validation
Nested cross-validation with glmnet
Outer cross-validation of SuperLearner model
Nested cross-validation for caret
One-hot encode
Outer cross-validation of selected models
Plot cross-validated glmnet alpha
SHAP importance beeswarm plot
Plot variable stability
Variable importance plot
Plot lambda across range of alphas
Plot precision-recall curve
Random forest filter
Partial Least Squares filter
Build precision-recall curve
Prediction wrappers to use fastshap with nestedcv
Predict method for cva.glmnet models
Predict from hsstan model fitted within cross-validation
Predict method for nestcv.glmnet fits
Summarise prediction performance metrics
Oversampling and undersampling
Random forest ranger filter
ReliefF filter
Repeated nested CV
SMOTE
Univariate filter for binary classification with mixed predictor datat...
Summarise variables
Supervised PCA plot
Outer training fold predictions
Build ROC curve from outer CV training folds
Summarise performance on outer training folds
Univariate filters
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Variable directionality
Variable stability
Calculate weights for class imbalance
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>.