parcor-package: Parcor: Estimation of partial correlations based on...

In parcor: Regularized estimation of partial correlation matrices

Description

This package contains different methods to estimate the matrix of partial correlations based on a (n x p) matrix X of observation. For low-dimensional settings (p>n), the matrix of partial correlations can be estimated based on p least-squares regression fits. However, in high-dimensional scenarios (p<n), theses least-squares problems are ill-posed and need to be regularized. This package contains four different regularized regression techniques for the estimation of the partial correlations: lasso, adaptive lasso, ridge regression, and Partial Least Squares. In addition, the package provides model selection for lasso, adaptive lasso and Ridge regression based on cross-validation.

Details

Package:	parcor
Type:	Package
Version:	0.2-6
Date:	2014-09-04
License:	GPL2 or newer
LazyLoad:	yes

Author(s)

Nicole Kraemer, Juliane Schaefer

Maintainer: Nicole Kraemer <[e-mail]>

References

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks with Gaussian Graphical Models", BMC Bioinformatics, 10:384

http://www.biomedcentral.com/1471-2105/10/384/

parcor documentation built on May 1, 2019, 9:10 p.m.