- Fix: ensure
`nfolds`

for final CV equals`n_inner_folds`

in`nestcv.glmnet()`

- 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.

- Improve
`plot_var_stability()`

to be more user friendly - Add
`top`

argument to shap plots

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

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

- 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()`

- 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`

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

- 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

- 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

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

- 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`

- Fix windows parallelisation bugs

- 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`

- 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()`

- 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

- 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

- 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

- 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`

.

- 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