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A User-friendly and Powerful R Analysis of Large-scale Datasets
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The huge Package for High-dimensional Undirected Graph Estimation in R.

Tuo Zhao1, Han Liu, Kathryn Roeder2

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA, TOURZHAO@JHU.EDU.

Journal of Machine Learning Research : JMLR
|February 3, 2016
PubMed
Summary

The huge R package simplifies estimating high-dimensional undirected graphs using advanced algorithms. It offers enhanced portability, broader model support, and improved scalability for complex network analysis.

Keywords:
data-dependent model selectionglassohigh-dimensional undirected graph estimationhugelossless screeninglossy screeningsemiparametric graph estimation

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Area of Science:

  • Computational Statistics
  • Network Analysis
  • Machine Learning

Background:

  • Estimating high-dimensional undirected graphs is crucial for understanding complex systems.
  • Existing tools like 'glasso' have limitations in portability and functionality.
  • Recent advancements in graphical model estimation require efficient software implementation.

Purpose of the Study:

  • Introduce the 'huge' R package for high-dimensional graph estimation.
  • Highlight its advantages over existing software.
  • Provide a versatile tool for researchers in statistics and machine learning.

Main Methods:

  • Implementation of recent graphical lasso algorithms (Friedman et al., 2007; Liu et al., 2009, 2012).
  • Development in C for enhanced portability and C++.
  • Incorporation of screening rules for scalability.

Main Results:

  • The 'huge' package offers improved portability and ease of modification compared to Fortran-based packages.
  • It supports Gaussian graphical models and high-dimensional semiparametric Gaussian copula models.
  • Includes data-dependent model selection, data generation, graph visualization, and a corrected graphical lasso algorithm.
  • Scalability is enhanced through lossless and lossy screening rules, balancing computational and statistical efficiency.

Conclusions:

  • The 'huge' package is a powerful and flexible tool for high-dimensional graph estimation.
  • It addresses limitations of existing methods, offering broader applicability and improved performance.
  • Facilitates advanced network analysis in various scientific domains.