Finding Modal Clusters in a Hierarchical fashion. This
clusters are extremely flexible in shape and are
computationally fast even in highe dimensions.

Perfroms Modal Clustering (MAC) including Hierarchical
Modal Clustering (HMAC) along with their parallel
implementation (PHMAC) over several processors.  These
model-based non-parametric clustering techniques can extract
clusters in very high dimensions with arbitrary density shapes.
By default clustering is performed over several resolutions and
the results are summarized as a hierarchical tree. Associated
plot functions are also provided. There is a package vignette
that provides many examples.

Performs Modal Clustering (MAC) including Hierarchical
Modal Clustering (HMAC) along with their parallel
implementation (PHMAC) over several processors.  These
model-based non-parametric clustering techniques can extract
clusters in very high dimensions with arbitrary density shapes.
By default clustering is performed over several resolutions and
the results are summarised as a hierarchical tree. Associated
plot functions are also provided. There is a package vignette
that provides many examples.

R (>= 2.14.0), mvtnorm, zoo, class, MASS

Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.

Bootstrap cross-validation for ROC, AUC and Brier score to
assess and compare predictions of binary status responses.

Bootstrap cross-validation for ROC, AUC and Brier score to assess and compare predictions of binary status responses.