{
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  "Package": "GPUmatrix",
  "Type": "Package",
  "Title": "Basic Linear Algebra with GPU",
  "Version": "1.0.3",
  "Authors@R": "c(person(\"Cesar\", \"Lobato-Fernandez\", role = c(\"aut\", \"ctb\")),\nperson(\"Juan\", \"A.Ferrer-Bonsoms\", role = \"aut\"),\nperson(\"Angel\", \"Rubio\", email = \"arubio@unav.es\",role = c(\"aut\",\"cre\")))",
  "Description": "GPUs are great resources for data analysis, especially in\nstatistics and linear algebra. Unfortunately, very few packages\nconnect R to the GPU, and none of them are transparent enough\nto run the computations on the GPU without substantial changes\nto the code. The maintenance of these packages is cumbersome:\nseveral of the earlier attempts have been removed from their\nrespective repositories. It would be desirable to have a\nproperly maintained R package that takes advantage of the GPU\nwith minimal changes to the existing code. We have developed\nthe 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics\nthe behavior of the Matrix package and extends R to use the GPU\nfor computations. It includes single(FP32) and double(FP64)\nprecision data types, and provides support for sparse matrices.\nIt is easy to learn, and requires very few code changes to\nperform the operations on the GPU. 'GPUmatrix' relies on either\nthe 'Torch' or 'Tensorflow' R packages to perform the GPU\noperations. We have demonstrated its usefulness for several\nstatistical applications and machine learning applications:\nnon-negative matrix factorization, logistic regression and\ngeneral linear models. We have also included a comparison of\nGPU and CPU performance on different matrix operations.",
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  "Date/Publication": "2026-03-26 10:56:40 UTC",
  "RemoteUrl": "https://github.com/arubio2/gpumatrix",
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  "Maintainer": "Angel Rubio <arubio@unav.es>",
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    "rowMaxs",
    "rowRanks",
    "to_dense",
    "to_sparse"
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      "page": "aperm",
      "title": "Array Transposition",
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        "t,gpu.matrix.torch-method",
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      ]
    },
    {
      "page": "apply",
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        "apply,gpu.matrix.tensorflow-method",
        "apply,gpu.matrix.torch-method",
        "apply-methods"
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    {
      "page": "c.GPUmatrix",
      "title": "Concatenate GPUmatrix objects",
      "topics": [
        "c.GPUmatrix"
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    },
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      "page": "cbind_rbind_methods",
      "title": "cbind_rbind_methods",
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        "cbind2,ANY,gpu.matrix.torch-method",
        "cbind2,gpu.matrix.tensorflow,ANY-method",
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      "page": "concatenate_gpu.matrix",
      "title": "concatenate_gpu.matrix",
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        "c,gpu.matrix.torch-method",
        "c,numMatrixLike-method",
        "c-methods"
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      "page": "cor_cov",
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        "cor,ANY,gpu.matrix.torch,ANY,ANY-method",
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        "cor,gpu.matrix.tensorflow,missing,missing,character-method",
        "cor,gpu.matrix.torch,ANY,ANY,ANY-method",
        "cor,gpu.matrix.torch,ANY,missing,character-method",
        "cor,gpu.matrix.torch,missing,ANY,ANY-method",
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        "cor,gpu.matrix.torch,missing,missing,missing-method",
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        "cov2cor,gpu.matrix.torch-method",
        "cov2cor-methods"
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    },
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      "title": "Kernel Density Estimation and Histograms",
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        "density,gpu.matrix.tensorflow-method",
        "density,gpu.matrix.torch-method",
        "density-methods",
        "hist",
        "hist,gpu.matrix.tensorflow-method",
        "hist,gpu.matrix.torch-method",
        "hist-methods"
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    },
    {
      "page": "det",
      "title": "Calculate the Determinant of a 'GPUMatrix'",
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        "det,gpu.matrix.tensorflow-method",
        "det,gpu.matrix.torch-method",
        "det-methods",
        "determinant",
        "determinant,gpu.matrix.tensorflow,logical-method",
        "determinant,gpu.matrix.tensorflow,missing-method",
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    {
      "page": "dim_and_names",
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    },
    {
      "page": "dist",
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    {
      "page": "expmGPU",
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        "expmGPU,gpu.matrix.tensorflow-method",
        "expmGPU,gpu.matrix.torch-method",
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    {
      "page": "gpu.matrix-class",
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    {
      "page": "GPUglm",
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      "page": "LR_GradientConjugate_gpumatrix",
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    {
      "page": "matrix_decomposition",
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    {
      "page": "head",
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        "head,gpu.matrix.tensorflow-method",
        "head,gpu.matrix.torch-method",
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    {
      "page": "matrix_ranges",
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        "colMaxs,gpu.matrix.torch-method",
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      "page": "summary.GPUglm",
      "title": "Summary for GPUglm objects",
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    {
      "page": "set_type_of_gpumatrix",
      "title": "Spicify type of 'GPUmatrix'",
      "topics": [
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  "_readme": "https://github.com/arubio2/gpumatrix/raw/HEAD/README.md",
  "_rundeps": [],
  "_vignettes": [
    {
      "source": "Vignette.Rmd",
      "filename": "Vignette.html",
      "title": "Get started with GPUmatrix package",
      "author": "Cesar Lobato-Fernandez, Juan A. Ferrer-Bonsoms & Ángel Rubio",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Abstract",
        "0 Installation",
        "0.1 Dependences",
        "For torch: (Link installation here)",
        "For Tensorflow: (Link installation here)",
        "For Apple Silicon",
        "0.2 GPUmatrix installation",
        "1 Initialization GPUmatrix",
        "1.1 Using GPUMatrix on CPU",
        "1.2 Using GPUMatrix on Apple Silicon",
        "1.3 Using GPUMatrix with Tensorflow",
        "",
        "2 Cast GPU matrices and data types",
        "2.1 Cast from other packages",
        "2.2 Data type and sparsity",
        "3 GPUmatrix functions",
        "3.1 Arithmetic and comparison operators",
        "3.2 Math operators",
        "3.2 Complex operators",
        "3.3 Other functions",
        "3.4 Function time comparison",
        "4. Statistical functions applications",
        "4.1 Non negative factorization of a matrix",
        "4.2 Logistic regression of large models",
        "4.3 General Linear Models",
        "References"
      ],
      "created": "2026-03-25 15:59:27",
      "modified": "2026-03-25 17:49:31",
      "commits": 3
    }
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