  In this paper, we present a new R package COREclust dedicated to the
detection of representative variables in high dimensional spaces with a
potentially limited number of observations. Variable sets detection is based on
an original graph clustering strategy denoted CORE-clustering algorithm that
detects CORE-clusters, i.e. variable sets having a user defined size range and
in which each variable is very similar to at least another variable.
Representative variables are then robustely estimate as the CORE-cluster
centers. This strategy is entirely coded in C++ and wrapped by R using the Rcpp
package. A particular effort has been dedicated to keep its algorithmic cost
reasonable so that it can be used on large datasets. After motivating our work,
we will explain the CORE-clustering algorithm as well as a greedy extension of
this algorithm. We will then present how to use it and results obtained on
synthetic and real data.
