## FSelectorRcpp 0.2.0

• Changed discretize argument all to TRUE.
• Added customBreaksControl for creating custom breaks in discretize function.
• discretize can be now evaluated with data as a first argument in the formula interface
• You can now use discretize(iris, Species ~ .) or discretize(Species ~ ., iris).
• discretize(iris, Species ~ .) seems to be more pipe friendly.
• discretize_transform allows applying the discretization cut points to the new data set.
• extract_discretize_transformer produces small object containing all cutpoints. It can be also used to transform the new data set.
• extract_discretize_transformer can be useful in ML pipelines where the training data needs to be discarded to save memory.

## FSelectorRcpp 0.1.8

• Removed support for the doSnow progress bar. We now use doParallel in our examples, which is more recommended than doSnow.

Bug fixes:

• Fixed build using Rcpp 0.12.12
• feature_search now returns proper structure.

## FSelectorRcpp 0.1.3

Bug fixes:

• Export C++ routine for tests.
• Use dontrun to hide problematic example (for CRAN).

## FSelectorRcpp 0.1.2

Bug fixes:

• Removed problematic links in vignettes.

## FSelectorRcpp 0.1.1

Bug fixes:

• Skip benchmark if the RTCGA.rnaseq package is not available.
• Minor fixes in vignettes.

## FSelectorRcpp 0.1.0

Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with sparse matrix support.

Provided functions

• discretize() with additional equalsizeControl() and mdlControl - discretize a range of numeric attributes in the dataset into nominal attributes. Minimum Description Length (MDL) method is set as the default control. There is also available equalsizeControl() method.
• information_gain() - algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute,
• feature_search() - a convenience wrapper for \code{greedy} and \code{exhaustive} feature selection algorithms that extract valuable attributes depending on the evaluation method (called evaluator),
• cut_attrs() - select attributes by their score/rank/weights, depending on the cutoff that may be specified by the percentage of the highest ranked attributes or by the number of the highest ranked attributes,
• to_formula() (misc) - create a formula object from a vector.