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HUGE: High Dimensional Undirected Graphical Model Estimation

Maintainers: Tuo Zhao and Han Liu.

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Software Description: The package "huge" provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline.

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Highlights

Estimating nonparanormal graphical models.

Important Features

  • 1 Instead of using Fortan, it is written in C.
  • 2 Fitting high dimensional Gausian copula models.
  • 3 Graph generation, data generation and visualization.
  • 4 Support lossless and lossy screening rules.
  • 5 Subsampling and Permutation based model selection.
  • 6 Sparse matrix representation for scalability.


    • SMART

    Sparse Multivariate Analysis via Rank Transformation.

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    Bigmatrix

    Tuning Insensitive Graph Estimation and Regression.

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    SAM

    Sparse Additive Machine.

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    BigTSP

    Top Scoring Pair based methods for classification.

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    Software to come

    Function 2.

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    Software to come

    Function 3.

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    Roadmap

    Reading Group

    We have biweekly reading group. The topics of this semeser include optimization in infinite dimensional space, homotopy algorithm, stochastic convex optimization, random matrix theory, and CUDA for GPU programming.

    Useful Links

    Get In Touch

    Department of Operations Research and Financial Engineering
    Sherred Hall 224
    Princeton University, NJ 08544
    Phone: +609 258 1788
    Email: hanliu@princeton.edu