Changelog

use reference cell parameterization to create dummy variables for factors. The reference level (default first level) does not have a dummy variable. A factor with k levels creates k-1 dummy variables.
add SVM to the list of algorithms that need all numeric variables, otherwise create dummy variables

add GitHub Action workflows for R CMD check, test coverage, and pkgdown
calculate AUC from yardstick
package DMwR has been archived, so use SMOTE implementation from package performanceEstimation
print error message if suggested package not available
fix sequential_eval() for n = 1 bootstrap case

use a “one-vs-all” SMOTE subsampling technique: over-sample each class vs. the other classes and combine the oversampled datasets to create the “balanced” dataset
new multiclass metric G-mean
use yardstick package for most evaluation metrics
pass seeds to caret::trainControl() for reproducible tuning (#48)
add roc_plot() for plotting multi-class ROC curves
add custom print method for objects returned from prediction(). The output was previously not informative and too long
add parameter seed_samp to splendid() to allow setting random seed before subsampling
splendid_convert() is now defunct. Use splendid_process() for a more comprehensive data pre-processing step. The new function can convert categorical variables to dummy variables as before. Added the ability to standardize continuous variables and apply sampling techniques to deal with class imbalance. Subsampling can only occur on the training set.
add parameter stratify to allow stratified bootstrap sampling on training set
use standard convention for confusion matrices: predicted in rows, reference in columns
replace MLmetrics::MultiLogLoss() with ModelMetrics::mlogLoss() in logloss() since it handles the case when the truth has a category with 0 counts but is represented in the probability matrix
add NPV and specificity to evaluation()

increase minimum R version to 3.6.0
move packages used for classification to Suggests to reduce the number of dependencies and are used conditionally
remove deprecated context() from tests
update roxygen and docs
internal functions deprecated and imported from new packages as needed
update vignette parameter descriptions

put the macro and micro averaged ROC curves at the end of legend in roc_plot()
suppress warnings in call to multiROC::multi_roc() after updates to stats:::regularize.values() in R-3.6.0 passes warn.collapsing = TRUE if there is no value for ties in stats::approx()
in sequential method, remove bootstrap iterations with an undefined F1-measure from average calculation
increase perc.over and perc.under in SMOTE subsampling to ensure the second to smallest class has > 0 cases
decrease minsplit in adaboost so fewer observations are needed to split a node in the rpart classifier
fix num_class in xgboost: number of classes should be taken from factor levels (some might be dropped from training set)
fix factor order in class_threshold() to take from column order of associated probability matrix

splendid 0.3.1.9000