Title: Kernel PC Algorithm for Causal Structure Detection
Description: Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.
Author: Petras Verbyla, Nina Ines Bertille Desgranges, Lorenz Wernisch
Imports: pcalg, energy, kernlab, parallel, mgcv, RSpectra, methods,

Title: Determination of K Using Peak Counts of Features for Clustering
Description: The input argument k which is the number of clusters is needed to start all of the partitioning clustering algorithms. In unsupervised learning applications, an optimal value of this argument is widely determined by using the internal validity indexes. Since these indexes suggest a k value which is computed on the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.

Description: The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary,  the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Imports: graphics, stats, utils, methods

Description: Functions that implement the known population median test.
Author: Matthew M Parks <map2085@med.cornell.edu>
Maintainer: Matthew M Parks <map2085@med.cornell.edu>
Imports: matrixStats, stats, methods, utils