qp {qp}R Documentation

The package 'qp': summary information

Description

This package provides functions for implementing the q-order partial-correlation graph search algorithm, q-partial, or qp, algorithm for short. The qp algorithm is a robust procedure for structure learning of undirected Gaussian graphical Markov models (UGGMMs) from "small n, large p" data, that is, multivariate normal data coming from a number of random variables p larger than the number of multidimensional data points n as in the case of, e.g., microarray data.

Data

Functions

The package provides an implementation of the procedures described by Castelo and Roverato (2006) and is a contribution to the gR-project described by Lauritzen (2002).

Authors

Robert Castelo, Departament de Ci`encies Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.

Alberto Roverato, Dipartimento di Scienze Statistiche, Universit`a di Bologna, Italy.

References

Lauritzen, S. L. (2002). gRaphical Models in R. R News, 3(2)39.

Castelo, R. and Roverato, A. (2006). A robust procedure for Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res., accepted.


[Package qp version 0.1-1 Index]