hmgm-package: High-dimensional mixed graphical models estimation

In hmgm: High-Dimensional Mixed Graphical Models Estimation

Description

A package for high-dimensional mixed graphical models estimation.

Details

Package:	hmgm
Type:	Package
Version:	0.3.0
Date:	2019-11-29
License:	GPL (>= 2)

The major function hmgm provides weighted lasso framework for high-dimensional mixed data graph estimation.

Another function pargroup identify all regions where groups intersect, make all variables in each overlapping region into a new group.

Author(s)

Mingyu Qi, Tianxi Li

References

Jie Cheng, Tianxi Li, Elizaveta Levina, and Ji Zhu.(2017) High-dimensional Mixed Graphical Models. Journal of Computational and Graphical Statistics 26.2 (2017): 367-378,https://arxiv.org/pdf/1304.2810.pdf
Simon, N., Friedman, J., Hastie,T., Tibshirani, R.(2011) Regularization Paths for Cox's ProportionalHazards Model via Coordinate Descent, Journal of Statistical Software, Vol.39(5) 1-13,https://www.jstatsoft.org/v39/i05/
Meinshausen, N. and Buhlmann, P. (2006) High dimensional graphs and variable selection with the lasso, Annals of Statistics, 34, 1436–1462., https://arxiv.org/pdf/math/0608017.pdf
Ravikumar, P., Wainwright, M., and Lafferty, J.(2010) High-dimensionalIsing model selection using l1-regularized logistic regression,Annals of Statistics, 38, 1287–1319.,https://arxiv.org/pdf/1010.0311.pdf
Liu, H., Han, F., Yuan, M., Lafferty, J., and Wasserman, L.(2012) High dimensional semiparametric Gaussian copula graphical models, Annals of Statistics, 40, 2293–2326., https://arxiv.org/pdf/1202.2169.pdf
Zhao, P., Rocha, G., and Yu, B.(2009) The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, 3468–3497., https://arxiv.org/pdf/0909.0411.pdf

hmgm documentation built on Jan. 13, 2021, 5:19 p.m.