Changes in version 1.2.2                        

  - In mlearning(), when data is not provided, model.args$data is now
    evaluated in the environment of the formula, if any, instead of
    parent.frame() (the previous behavior is used as default fallback).

                 Changes in version 1.2.1 (2023-08-30)                  

  - 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.

                        Changes in version 1.2.0                        

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

                 Changes in version 1.1.1 (2022-04-26)                  

  - The description is extended.

  - A {pkgdown} site is added.

                        Changes in version 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 &&).

                        Changes in version 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.

                        Changes in version 1.0.6                        

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

                        Changes in version 1.0.5                        

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

                        Changes in version 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.

                        Changes in version 1.0.3                        

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