News

nestedcv 0.7.10

29/07/2024

• Fixed oversized SVG figures in vignette.
• Fixed bug in computing multi-class balanced accuracy. This is now calculated as the mean of the Recall for each class.
• Added multi-class Matthew’s correlation coefficient (MCC) and multi-class F1 macro score.

nestedcv 0.7.9

15/04/2024

Important change

• Rsquared performance metric for regression/continuous outcomes was previously calculated using defaultSummary() function from caret which uses the square of Pearson correlation coefficient (r-squared), instead of the correct coefficient of determination which is calculated as 1 - rss/tss, where rss = residual sum of squares, tss = total sum of squares. The correct formula for R-squared is now being applied.

Bugfix

• Prevent bug if x is a single predictor.

Other updates

• Updated documentation for custom filter functions.

nestedcv 0.7.8

11/03/2024

• Added prc() which enables easy building of precision-recall curves from ‘nestedcv’ models and repeatcv() results.
• Added predict method for cva.glmnet.
• Removed magrittr as an imported package. The standard R pipe |> can be used instead.
• Added metrics() which gives additional performance metrics for binary classification models such as F1 score, Matthew’s correlation coefficient and precision recall AUC.
• Added pls_filter() which uses partial least squares regression to filter features.
• Enabled parallelisation over repeats in repeatedcv() leading to significant improvement in speed.

nestedcv 0.7.4

30/01/2024

• Fixed issue with xgboost on linux/windows with parallel processing in nestcv.train(). If argument cv.cores >1, openMP multithreading is now disabled, which prevents caret models xgbTree and xgbLinear from crashing, and allows them to be parallelised efficiently over the outer CV loops.
• Improvements to var_stability() and its plots.
• Fixed major bug in multivariate Gaussian and Cox models in nestcv.glmnet()

nestedcv 0.7.3

30/11/2023

• Added new feature repeatcv() to apply repeated nested CV to the main nestedcv model functions for robust measurement of model performance.

nestedcv 0.7.2

17/11/2023

• Added new feature via modifyX argument to all nestedcv models. This allows more powerful manipulation of the predictors such as scaling, imputing missing values, adding extra columns through variable manipulations. Importantly these are applied to train and test input data separately.
• Added predict() function for nestcv.SuperLearner()
• Added pred_SuperLearner wrapper for use with fastshap::explain
• Fixed parallelisation of nestcv.SuperLearner() on windows.

nestedcv 0.7.0

18/10/2023

• Added support for multivariate Gaussian and Cox models in nestcv.glmnet()

nestedcv 0.6.9

15/08/2023

New features

• Added argument verbose in nestcv.train(), nestcv.glmnet() and outercv()to show progress.
• Added argument multicore_fork in nestcv.train() and outercv() to allow choice of parallelisation between forked multicore processing using mclapply or non-forked using parLapply. This can help prevent errors with certain multithreaded caret models e.g. model = "xgbTree".
• In one_hot() changed all_levels argument default to FALSE to be compatible with regression models by default.
• Add coefficient column to lm_filter() full results table

Bug fixes

• Fixed significant bug in lm_filter() where variables with zero variance were incorrectly reporting very low p-values in linear models instead of returning NA. This is due to how rank deficient models are handled by RcppEigen::fastLmPure. Default method for fastLmPure has been changed to 0 to allow detection of rank deficient models.
• Fixed bug in weight() caused by NA. Allow weight() to tolerate character vectors.

nestedcv 0.6.7

01/07/2023

New features

• Better handling of dataframes in filters. keep_factors option has been added to filters to control filtering of factors with 3 or more levels.
• Added one_hot() for fast one-hot encoding of factors and character columns by creating dummy variables.
• Added stat_filter() which applies univariate filtering to dataframes with mixed datatype (continuous & categorical combined).
• Changed one-way ANOVA test in anova_filter() from Rfast::ftests() to matrixTests::col_oneway_welch() for much better accuracy

Bug fixes

• Fixed bug caused by use of weights with nestcv.train() (Matt Siggins suggestion)

nestedcv 0.6.6

07/06/2023

New features

• Added n_inner_folds argument to nestcv.train() to make it easier to set the number of inner CV folds, and inner_folds argument which enables setting the inner CV fold indices directly (suggestion Aline Wildberger)

Bug fixes

• Fixed error in plot_shap_beeswarm() caused by change in fastshap 0.1.0 output from tibble to matrix
• Fixed bug with categorical features and nestcv.train()

nestedcv 0.6.4

29/05/2023

New features

• Add argument pass_outer_folds to both nestcv.glmnet and nestcv.train: this enables passing of passing of outer CV fold indices stored in outer_folds to the final round of CV. Note this can only work if n_outer_folds = number of inner CV folds and balancing is not applied so that y is a consistent length.

Bug fixes

• Fix: ensure nfolds for final CV equals n_inner_folds in nestcv.glmnet()

nestedcv 0.6.3

17/05/2023

• Improve plot_var_stability() to be more user friendly
• Add top argument to shap plots

nestedcv 0.6.2

15/05/2023

• Modified examples and vignette in anticipation of new version of fastshap 0.1.0

nestedcv 0.6.1

15/04/2023

• Add vignette for variable stability and SHAP value analysis
• Refine variable stability and shap plots

nestedcv 0.6.0

19/03/2023

• Switch some packages from Imports to Suggests to make basic installation simpler.
• Provide helper prediction wrapper functions to make it easier to use package fastshap for calculating SHAP values.
• Add force_vars argument to glmnet_filter()
• Add ranger_filter()

nestedcv 0.5.2

17/02/2023

• Disable printing in nestcv.train() from models such as gbm. This fixes multicore bug when using standard R gui on mac/linux.
• Bugfix if nestcv.glmnet() model has 0 or 1 coefficients.
• Add multiclass AUC for multinomial classification.

nestedcv 0.5.0

23/01/2023

• nestedcv models now return xsub containing a subset of the predictor matrix x with filtered variables across outer folds and the final fit
• boxplot_model() no longer needs the predictor matrix to be specified as it is contained in xsub in nestedcv models
• boxplot_model() now works for all nestedcv model types
• Add function var_stability() to assess variance and stability of variable importance across outer folds, and directionality for binary outcome
• Add function plot_var_stability() to plot variable stability across outer folds
• Add finalCV = NA option which skips fitting the final model completely. This gives a useful speed boost if performance metrics are all that is needed.
• model argument in outercv now prefers a character value instead of a function for the model to be fitted
• Bugfixes

nestedcv 0.4.6

07/12/2022

• Add check model exists in outercv
• Perform final model fit first in nestcv.train which improves error detection in caret. So nestcv.train can be run in multicore mode straightaway.
• Removes predictors with variance = 0
• Fix bug caused by filter p-values = NA

nestedcv 0.4.4

05/12/2022

• Add confusion matrix to results summaries for classification
• Fix bugs in extraction of inner CV predictions for nestcv.glmnet
• Fix multinomial nestcv.glmnet
• Add outer_train_predict argument to enable saving of predictions on outer training folds
• Add function train_preds to obtain outer training fold predictions
• Add function train_summary to show performance metrics on outer training folds

nestedcv 0.4.1

12/11/2022

• Add examples of imbalance datasets
• Fix rowname bug in smote()

nestedcv 0.4.0

28/09/2022

• Add support for nested CV on ensemble models from SuperLearner package
• Final CV on whole data is now the default in nestcv.train and nestcv.glmnet

nestedcv 0.3.6

18/09/2022

• Fix windows parallelisation bugs

nestedcv 0.3.5

16/09/2022

• Fix bug in nestcv.train for caret models with tuning parameters which are factors
• Fix bug in nestcv.train for caret models using regression
• Add option in nestcv.train and nestcv.glmnet to tune final model parameters using a final round of CV on the whole dataset
• Fix bugs in LOOCV
• Add balancing to final model fitting
• Add case weights to nestcv.train and outercv

nestedcv 0.3.0

07/09/2022

• Add randomsample() to handle class imbalance using random over/undersampling
• Add smote() for SMOTE algorithm for increasing minority class data
• Add bootstrap wrapper to filters, e.g. boot_ttest()

nestedcv 0.2.6

02/09/2022

• Final lambda in nestcv.glmnet() is mean of best lambdas on log scale
• Added plot_varImp for plotting variable importance for nestcv.glmnet final models

nestedcv 0.2.4

19/07/2022

• Corrected handling of multinomial models in nestcv.glmnet()
• Align lambda in cva.glmnet()
• Improve plotting of error bars in plot.cva.glmnet
• Bugfix: plot of single alphaSet in plot.cva.glmnet
• Updated documentation and vignette

nestedcv 0.2.1

15/06/2022

• Parallelisation on windows added
• hsstan model has been added (Athina Spiliopoulou)
• outer_folds can be specified for consistent model comparisons
• Checks on x, y added
• NA handling
• summary and print methods
• Implemented LOOCV
• Collinearity filter
• Implement lm and glm as models in outercv()
• Runnable examples have been added throughout

nestedcv 0.0.9100

02/03/2022

• Major update to include nestedcv.train function which adds nested CV to the train function of caret
• Note passing of extra arguments to filter functions specified by filterFUN is no longer done through ... but with a list of arguments passed through a new argument filter_options.

nestedcv 0.0.9003

02/03/2022

• Initial build of nestedcv
• Added outercv.rf function for measuring performance of rf
• Added cv.rf for tuning mtry parameter
• Added plot_caret for plotting caret objects with error bars on the tuning metric