RcppML/DESCRIPTION at main · zdebruine/RcppML · GitHub

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Package: RcppML
Type: Package
Title: Fast Non-Negative Matrix Factorization and Divisive Clustering
Version: 1.0.0
Date: 2026-03-10
Authors@R: person("Zachary", "DeBruine",
    email = "zacharydebruine@gmail.com",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0003-2234-4827"))
Description: High-performance non-negative matrix factorization (NMF),
    singular value decomposition (SVD), and divisive clustering for large
    sparse and dense matrices. Implements alternating least squares with
    coordinate descent and Cholesky NNLS solvers, diagonal scaling for
    interpretable factors, cross-validation for automatic rank selection,
    multiple distribution-based losses (Gaussian, Poisson, Generalized Poisson,
    Negative Binomial, Gamma, Inverse Gaussian, Tweedie) via iteratively
    reweighted least squares, regularization (L1, L2, L21, angular, graph
    Laplacian), and optional GPU acceleration via CUDA. Includes divisive
    clustering via recursive rank-2 factorization, consensus clustering,
    and the StreamPress compressed sparse matrix format. Methods are
    described in DeBruine, Melcher, and Triche (2021)
    <doi:10.1101/2021.09.01.458620>.
Depends:
    R (>= 4.1.0),
    Matrix
License: GPL (>= 3)
Imports:
    Rcpp,
    methods,
    stats,
    utils
LinkingTo:
    Rcpp,
    RcppEigen
RoxygenNote: 7.3.3
Suggests:
    ggplot2,
    hdf5r,
    jsonlite,
    knitr,
    pheatmap,
    randomForest,
    RColorBrewer,
    rmarkdown,
    patchwork,
    plotly,
    scales,
    Seurat,
    SeuratData,
    SeuratObject,
    testthat (>= 3.0.0),
    uwot,
    viridis
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: true
LazyDataCompression: xz
URL: https://github.com/zdebruine/RcppML
BugReports: https://github.com/zdebruine/RcppML/issues
SystemRequirements: CUDA Toolkit >= 11.0 (optional, for GPU acceleration)
Encoding: UTF-8