\name{ClassifyRnews}
\title{ClassifyR News}
\encoding{UTF-8}

\section{Version 1.4.0}{\itemize{

\item
Weighted voting mode that uses the distance from an observation to the nearest crossover point
of the class densities added.
\item
Bartlett Test selection function included.
\item
New class SelectResult. rankPlot and selectionPlot can additionally work with lists
of SelectResult objects. All feature selection functions now return a SelectResult object
or a list of them.
\item
priorSelection is a new selection function for using features selected in a prior cross
validation for a new dataset classification.
\item
New weighted voting mode, where the weight is the distance of the x value from the
nearest crossover point of the two densities. Useful for predictions with skewed 
features.
}}

\section{Version 1.2.0}{\itemize{

\item
More classification flexibility, now with parameter tuning integrated into the process.
\item
New performance evaluation functions, such as a ROC curve and a performance plot.
\item
Some existing predictor functions are able to return class scores, not just class labels.
}}

\section{Version 1.0.0}{\itemize{

\item
First release of the package, which allows parallelised and customised classification, with many convenient performance evaluation functions.
}}