A suite of machine learning algorithms written in C++ with the R
interface contains several learning techniques for classification and regression.
Predictive models include e.g., classification and regression trees with
optional constructive induction and models in the leaves, random forests, kNN,
naive Bayes, and locally weighted regression. All predictions obtained with these
models can be explained and visualized with the 'ExplainPrediction' package.
This package is especially strong in feature evaluation where it contains several variants of
Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini,
information gain, MDL, and DKM. These methods can be used for feature selection
or discretization of numeric attributes.
The OrdEval algorithm and its visualization is used for evaluation
of data sets with ordinal features and class, enabling analysis according to the
Kano model of customer satisfaction.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.

We proposed a new model-based clustering method-the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. This package includes the implementation for two such clustering procedures: (1) clustering of linear models (CLM), and (2) clustering of linear mixed models (CLMM).

We proposed a new model-based clustering method, called the clustering of
regression models method(CORM), which groups genes that share a similar
relationship to the covariate(s). This method provides a unified approach
for a family of clustering procedures and can be applied to data collected
with various experimental designs. This package includes the implementation
for two such clustering procedures: (1) the Clustering of Linear Models
(CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
The Clustering of Regression Models Method