huge-package: High-Dimensional Undirected Graph Estimation

In huge: High-Dimensional Undirected Graph Estimation

Description

A package for high-dimensional undirected graph estimation

Details

Package:	huge
Type:	Package
Version:	1.2.7
Date:	2015-09-14
License:	GPL-2
LazyLoad:	yes

The package "huge" provides 8 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 correlation thresholding graph estimation. Please refer to huge.
(4) The Meinshausen-Buhlmann graph estimation. Please refer to huge.
(5) The graphical Lasso algorithm using lossless screening rule. Please refer and huge.

**Both (4) and (5) can be further accelerated by the lossy screening rule preselecting the neighborhood of each node via thresholding sample correlation.
(6) The model selection using the stability approach to regularization selection. Please refer to huge.select.
(7) The model selection using the rotation information criterion. Please refer to huge.select.
(8) The model selection using the extended Bayesian information criterion. Please refer to huge.select.

Author(s)

Tuo Zhao, Han Liu, Haoming Jiang, Kathryn Roeder, John Lafferty, and Larry Wasserman
Maintainers: Haoming Jiang<[e-mail]>;

References

1. T. Zhao and H. Liu. The huge Package for High-dimensional Undirected Graph Estimation in R. Journal of Machine Learning Research, 2012
2. H. Liu, F. Han, M. Yuan, J. Lafferty and L. Wasserman. High Dimensional Semiparametric Gaussian Copula Graphical Models. Annals of Statistics,2012
3. D. Witten and J. Friedman. New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics, to appear, 2011. 4. Han Liu, Kathryn Roeder and Larry Wasserman. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems, 2010.
5. R. Foygel and M. Drton. Extended bayesian information criteria for gaussian graphical models. Advances in Neural Information Processing Systems, 2010.
6. H. Liu, J. Lafferty and L. Wasserman. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research, 2009
7. J. Fan and J. Lv. Sure independence screening for ultra-high dimensional feature space (with discussion). Journal of Royal Statistical Society B, 2008.
8. O. Banerjee, L. E. Ghaoui, A. d'Aspremont: Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. Journal of Machine Learning Research, 2008.
9. J. Friedman, T. Hastie and R. Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 2008.
10. J. Friedman, T. Hastie and R. Tibshirani. Sparse inverse covariance estimation with the lasso, Biostatistics, 2007.
11. N. Meinshausen and P. Buhlmann. High-dimensional Graphs and Variable Selection with the Lasso. The Annals of Statistics, 2006.

See Also

huge.generator, huge.npn, huge, huge.plot and huge.roc

huge documentation built on July 1, 2021, 1:06 a.m.