Open-Source Software

One of our group's missions is to develop open-source software that makes advanced statistical machine learning methods accessible to practitioners. We believe open-source software packages are crucial for knowledge sharing, bridging ML with big data science, and fostering an open-source culture. We are proud that our software has been downloaded over 80,000 times (as of October 2024), based on R-CRAN tracking. We welcome your feedback and suggestions as you use our software.

  • tensorregress
    R package for Supervised Tensor Decomposition with Side Information
    Downloads: 20,976

  • tensorssparse
    R package for Multiway Clustering via Tensor Block Models
    Downloads: 17,736

  • TensorComplete
    R package for Tensor Noise Reduction and Completion Methods
    Downloads: 10,201

  • TraceAssist
    R package for Nonparametric Trace Regression via Sign Series Representation
    Downloads: 8,586

  • dTBM
    R package for Multi-Way Spherical Clustering via Degree-Corrected Tensor Block Models
    Downloads: 6,968

  • SmoothTensor
    R package for Smooth Tensor Estimation Methods
    Downloads: 6,881

  • snQTL
    R package for Spectral QTL Mapping of Joint Differential Networks

  • SCENT
    R/Matlab packages for Simultaneous Clustering and Estimation of Networks

  • SignT
    R package for Nonparametric Tensor Completion via Sign Series

  • OrdinalT
    R package for Noise Reduction and Completion from Ordinal Tensor Data

  • BinaryT
    R package for Low-Rank Tensor Estimation from Binary Observations

  • TM-SVD
    Matlab package for Efficient Tensor Decomposition

  • Multi-Cluster
    Matlab package for Three-Way Clustering of Gene Expression Tensorial Data

  • ATOMM
    C package for Two-Organism Mixed-Effects Model Association Analysis

  • G-STRATEGY
    C package for Genotyping Selection Strategy Based on Phenotypes and Pedigrees