mlearning 1.2.1

• 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.

mlearning 1.2.0

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

mlearning 1.1.1

• The description is extended.

• A {pkgdown} site is added.

mlearning 1.1.0

• 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 &&).

mlearning 1.0.7

• 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.

mlearning 1.0.6

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

mlearning 1.0.5

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

mlearning 1.0.4

• 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.

mlearning 1.0.3

• NEWS.md file added. Repository moved to GitHub.