Literature DB >> 26834510

The huge Package for High-dimensional Undirected Graph Estimation in R.

Tuo Zhao1, Han Liu, Kathryn Roeder2, John Lafferty, Larry Wasserman.   

Abstract

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.

Entities:  

Keywords:  data-dependent model selection; glasso; high-dimensional undirected graph estimation; huge; lossless screening; lossy screening; semiparametric graph estimation

Year:  2012        PMID: 26834510      PMCID: PMC4729207     

Source DB:  PubMed          Journal:  J Mach Learn Res        ISSN: 1532-4435            Impact factor:   3.654


  4 in total

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Journal:  Adv Neural Inf Process Syst       Date:  2010-12-31

2.  Sparse inverse covariance estimation with the graphical lasso.

Authors:  Jerome Friedman; Trevor Hastie; Robert Tibshirani
Journal:  Biostatistics       Date:  2007-12-12       Impact factor: 5.899

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4.  Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors:  Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal:  J Stat Softw       Date:  2010       Impact factor: 6.440

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