huge-package: High-dimensional Undirected Graph Estimation
Description
A package for high-dimensional undirected graph estimationDetails
ll{
Package: huge
Type: Package
Version: 1.0.1
Date: 2011-04-10
License: GPL-2
LazyLoad: yes
}
The package "huge" provides 9 main functions:
(1) the data generator creates random samples from multivariate normal distributions with different graph structures. Please refer to huge.generator.
(2) the NonparaNormal (NPN) transformation helps relax the normality assumption. Please refer to huge.NPN.
(3) The Graph SURE Screening (GSS) helps reduce the computation burden by preselecting the neighborhood of each node via thresholding sample correlation. Please refer to huge.MBGEL.
(4) The Graph Estimation via Correlation Thresholding (GECT).Please refer to huge.GECT and huge.
(5) The Meinshausen & Buhlmann Graph Estimation via Lasso(MBGEL). Please refer to huge.MBGEL and huge.
(6) The slightly modified Graphical Lasso (GLASSO) using sparse matrix representation. Please refer to huge.glassoM and huge.
(7) The model selection using the Stability Approach to Regularization Selection(StARS). Please refer to huge.select and lasso.stars.
(8) The model selection using the Rotation Information Criterion (RIC). Please refer to huge.select.
(9) The model selection using the Extended Bayesian Information Criterion(EBIC). Please refer to huge.select.References
1.Tuo Zhao and Han Liu. HUGE: A Package for High-dimensional Undirected Graph Estimation. Technical Report, Carnegie Mellon University, 2010
2.Han Liu, Kathryn Roeder and Larry Wasserman. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems(NIPS), 2010.
3.Jerome Friedman, Trevor Hastie and Rob Tibshirani. Applications of the lasso and grouped lasso to the estimation of sparse graphical models, Technical Report, Stanford University, 2010.
4.Han Liu, John Lafferty and Larry Wasserman. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research (JMLR), 2009
5.Jianqing Fan and Jinchi Lv. Sure independence screening for ultra-high dimensional feature space (with discussion). Journal of Royal Statistical Society B, 2008.
6.Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont: Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. Journal of Machine Learning Research (JMLR), 2008.
7.Jiahua Chen and Zehua Chen. Extended Bayesian information criterion for model selection with large model space. Biometrika, 2008.
8.Jerome Friedman, Trevor Hastie and Robert Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 2008.
9.Jerome Friedman, Trevor Hastie and Robert Tibshirani. Sparse inverse covariance estimation with the lasso, Biostatistics, 2007.
10.Nicolai Meinshausen and Peter Buhlmann. High-dimensional Graphs and Variable Selection with the Lasso. The Annals of Statistics, 2006.