Changelog • mlearning

Documentation is refactored using Roxygen2 and considerably enhanced.
All camelCase function names now have their equivalence in snake_case, e.g., mlRforest -> ml_rforest(), or confusionImage() -> confusion_image() in order to adapt to the coding preferences of the user.
GitHub repository modernized ans Hex logo added.

The mlRpart() function implements rpart::rpart() for using decision trees.

The description is extended.
A {pkgdown} site is added.

mlKnn() is implemented for K-nearest neighbors.
Several adjustments were required for compatibility with R 4.2.0 (it is not allowed any more to use vectors > 1 with || and &&).

When predict() was applied to an mlearning object build with full formula (not the short one var ~ .), if the dependent variable was not in newdata =, an error message was raised (although this variable is not necessary at this point). Bug identified by Damien Dumont, and corrected.

In mlSvm.formula(), arguments scale=, type=, kernel= and classwt= were not correctly used. Corrected.

In mlLvq() providing size = or prior = led to an lvq object not found message. Corrected.

Sometimes, data was not found (e.g., when called inside a {learnr} tutorial).
In mlearning(), data is forced with as.data.frame() (tibbles are not supported internally).
In the mlXXX() function, it was not possible to indicate something like mlLda(data = iris, Species ~ .). Solved by adding train = argument in mlXXX().
In summary.confusion() produced an error if more than one type = was provided.

NEWS.md file added. Repository moved to GitHub.