nestedcv0.7.10 package

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>.

Maintainer: Myles Lewis License: MIT + file LICENSE Last published: 2024-08-16