{
  "_id": "6a23c9b5530b9bc726be11bb",
  "Package": "ePCR",
  "Type": "Package",
  "Title": "Ensemble Penalized Cox Regression for Survival Prediction",
  "Version": "0.11.0",
  "Date": "2024-02-18",
  "Author": "Teemu Daniel Laajala <teelaa@utu.fi> [aut, cre], Mika\nMurtojarvi <mianmu2@hotmail.com> [ctb]",
  "Maintainer": "Teemu Daniel Laajala <teelaa@utu.fi>",
  "Description": "The top-performing ensemble-based Penalized Cox Regression\n(ePCR) framework developed during the DREAM 9.5 mCRPC Prostate\nCancer Challenge\n<https://www.synapse.org/ProstateCancerChallenge> presented in\nGuinney J, Wang T, Laajala TD, et al. (2017)\n<doi:10.1016/S1470-2045(16)30560-5> is provided here-in,\ntogether with the corresponding follow-up work. While initially\naimed at modeling the most advanced stage of prostate cancer,\nmetastatic Castration-Resistant Prostate Cancer (mCRPC), the\nmodeling framework has subsequently been extended to cover also\nthe non-metastatic form of advanced prostate cancer (CRPC).\nReadily fitted ensemble-based model S4-objects are provided,\nand a simulated example dataset based on a real-life cohort is\nprovided from the Turku University Hospital, to illustrate the\nuse of the package. Functionality of the ePCR methodology\nrelies on constructing ensembles of strata in patient cohorts\nand averaging over them, with each ensemble member consisting\nof a highly optimized penalized/regularized Cox regression\nmodel. Various cross-validation and other modeling schema are\nprovided for constructing novel model objects.",
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  "Repository": "https://syksy.r-universe.dev",
  "Date/Publication": "2024-02-19 11:47:55 UTC",
  "RemoteUrl": "https://github.com/syksy/epcr",
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    "User": "root"
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    "message": "Faster checking with shorter alpha, with version bump\n",
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      ]
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    {
      "page": "conforminput",
      "title": "Conform the dimensions of a new input data matrix to a readily fitted PEP or PSP object",
      "topics": [
        "conforminput"
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      "title": "Function that creates customized cross-validation folds",
      "topics": [
        "cv"
      ]
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    {
      "page": "cv.alpha",
      "title": "Cross-validation runs for risk predition at a single value of alpha",
      "topics": [
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      ]
    },
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      "page": "cv.grid",
      "title": "Cross-validation runs for risk predition for a grid of predetermined alpha values and their conditional lambda values",
      "topics": [
        "cv.grid"
      ]
    },
    {
      "page": "DREAM",
      "title": "FIMM-UTU DREAM winning implementation of an ensemble of Penalized Cox Regression models for mCPRC research (ePCR)",
      "topics": [
        "DREAM"
      ]
    },
    {
      "page": "ePCR",
      "title": "Ensemble Penalized Cox Regression Modeling for Overall Survival and Time-to-Event Prediction in Advanced Prostate Cancer",
      "topics": [
        "ePCR"
      ]
    },
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      "page": "heatcv",
      "title": "Plot a heatmap of the prediction performance statistic as a function of lambda and alpha combinations",
      "topics": [
        "heatcv"
      ]
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    {
      "page": "integrateRegCurve",
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      "topics": [
        "integrateRegCurve"
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    },
    {
      "page": "interact.all",
      "title": "Compute all pairwise interactions between the columns of a data matrix",
      "topics": [
        "interact.all"
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    },
    {
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      "topics": [
        "interact.part"
      ]
    },
    {
      "page": "meanrank",
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      "topics": [
        "meanrank"
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    },
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      "page": "NelsonAalen",
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        "PEP-class"
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        "predict,PEP-method",
        "print,PEP-method"
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      "page": "PSP-class",
      "title": "Penalized Single Predictor (PSP) S4-class as a member of PEP-ensembles",
      "topics": [
        "PSP-class"
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    {
      "page": "PSP-methods",
      "title": "PSP-methods",
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        "plot,PSP-method",
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        "print,PSP-method",
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        "PSP.BOX,PSP,ANY-method",
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        "PSP.CSP,PSP-method",
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        "PSP.NA",
        "PSP.NA,PSP,ANY-method",
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        "PSP.PCA,PSP,ANY-method",
        "PSP.PCA,PSP-method"
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    },
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      "page": "score.cindex",
      "title": "Scoring function for evaluating survival prediction through concordance index (c-index)",
      "topics": [
        "score.cindex"
      ]
    },
    {
      "page": "score.iAUC",
      "title": "Scoring function for evaluating survival prediction by time-wise integrated AUC",
      "topics": [
        "score.iAUC"
      ]
    },
    {
      "page": "TimeSurvProb",
      "title": "Predict cumulative survival probabilities for new data at given time points",
      "topics": [
        "TimeSurvProb"
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    },
    {
      "page": "TYKS",
      "title": "ePCR model fitted to the Turku University Hospital cohorts (all features)",
      "topics": [
        "TYKS"
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    },
    {
      "page": "TYKS_reduced",
      "title": "ePCR model fitted to the Turku University Hospital cohorts (features derived from text mining only)",
      "topics": [
        "TYKS_reduced"
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      "page": "TYKSSIMU",
      "title": "TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital",
      "topics": [
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        "xMEDISIMU",
        "xTEXTSIMU",
        "yMEDISIMU",
        "yTEXTSIMU"
      ]
    },
    {
      "page": "zt",
      "title": "Extended function for z-transformation, filling non-finite values and changes column names at will",
      "topics": [
        "zt"
      ]
    }
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