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  "Title": "Standard regression functions in R enabled for parallel\nprocessing over large data-frames",
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  "Maintainer": "Kevin Blighe <kevin@clinicalbioinformatics.co.uk>",
  "Description": "In many analyses, a large amount of variables have to be\ntested independently against the trait/endpoint of interest,\nand also adjusted for covariates and confounding factors at the\nsame time. The major bottleneck in these is the amount of time\nthat it takes to complete these analyses. With RegParallel, a\nlarge number of tests can be performed simultaneously. On a\n12-core system, 144 variables can be tested simultaneously,\nwith 1000s of variables processed in a matter of seconds via\n'nested' parallel processing. Works for logistic regression,\nlinear regression, conditional logistic regression, Cox\nproportional hazards and survival models, and Bayesian logistic\nregression. Also caters for generalised linear models that\nutilise survey weights created by the 'survey' CRAN package and\nthat utilise 'survey::svyglm'.",
  "License": "GPL-3",
  "URL": "https://github.com/kevinblighe/RegParallel",
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  "Repository": "Bioconductor 3.23",
  "Date/Publication": "2026-04-28 12:32:16 UTC",
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  "Author": "Kevin Blighe [aut, cre],\nSarega Gurudas [ctb],\nJessica Lasky-Su [aut]",
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      "title": "RegParallel: Standard regression functions in R enabled for parallel processing over large data-frames.",
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      "title": "Standard regression functions in R enabled for parallel processing over large data-frames - Bayesian logistic regression",
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      "topics": [
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    {
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      "page": "lmParallel",
      "title": "Standard regression functions in R enabled for parallel processing over large data-frames - linear model.",
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    {
      "page": "RegParallel",
      "title": "Standard regression functions in R enabled for parallel processing over large data-frames",
      "topics": [
        "RegParallel"
      ]
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      "title": "Standard regression functions in R enabled for parallel processing over large data-frames - generalised linear model, with survey weights",
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      "source": "RegParallel.Rmd",
      "filename": "RegParallel.html",
      "title": "Standard regression functions in R enabled for parallel processing over large data-frames",
      "author": "Kevin Blighe, Jessica Lasky-Su",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "1. Download the package from Bioconductor",
        "2. Load the package into R session",
        "Quick start",
        "Perform the most basic logistic regression analysis",
        "Perform a basic linear regression",
        "Survival analysis via Cox Proportional Hazards regression",
        "Perform a conditional logistic regression",
        "Advanced features",
        "Speed up processing",
        "~2000 tests; blocksize, 500; cores, 2; nestedParallel, TRUE",
        "~2000 tests; blocksize, 500; cores, 2; nestedParallel, FALSE",
        "~40000 tests; blocksize, 2000; cores, 2; nestedParallel, TRUE",
        "~40000 tests; blocksize, 2000; cores, 2; nestedParallel, FALSE",
        "~40000 tests; blocksize, 5000; cores, 3; nestedParallel, TRUE",
        "Modify confidence intervals",
        "Remove some terms from output / include the intercept",
        "Acknowledgments",
        "Session info",
        "References"
      ],
      "created": "2018-09-24 20:55:23",
      "modified": "2021-10-01 23:16:39",
      "commits": 30
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