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  "Title": "Variable Selection in Finite Mixture of AFT Regression and FMR\nModels",
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  "Description": "The package obtains parameter estimation, i.e., maximum\nlikelihood estimators (MLE), via the Expectation-Maximization\n(EM) algorithm for the Finite Mixture of Regression (FMR)\nmodels with Normal distribution, and MLE for the Finite Mixture\nof Accelerated Failure Time Regression (FMAFTR) subject to\nright censoring with Log-Normal and Weibull distributions via\nthe EM algorithm and the Newton-Raphson algorithm (for Weibull\ndistribution). More importantly, the package obtains the\nmaximum penalized likelihood (MPLE) for both FMR and FMAFTR\nmodels (collectively called FMRs). A component-wise tuning\nparameter selection based on a component-wise BIC is\nimplemented in the package. Furthermore, this package provides\nRidge Regression and Elastic Net.",
  "Date": "2023-05-16",
  "biocViews": "Survival, Regression, DimensionReduction",
  "License": "GPL-3",
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  "Maintainer": "Farhad Shokoohi <shokoohi@icloud.com>",
  "Repository": "https://bioc.r-universe.dev",
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    "dispersion",
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    "fmrs.gendata",
    "fmrs.mle",
    "fmrs.tunsel",
    "fmrs.varsel",
    "fmrstunpar",
    "logLik",
    "mixProp",
    "ncomp",
    "ncov",
    "nobs",
    "residuals",
    "summary",
    "weights"
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      "title": "Variable Selection in Finite Mixture of AFT Regression and FMR Models",
      "topics": [
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        "fmrs"
      ]
    },
    {
      "page": "BIC-methods",
      "title": "BIC method",
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        "BIC",
        "BIC,BIC-method",
        "BIC,fmrsfit-method"
      ]
    },
    {
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        "coefficients,coefficients-method",
        "coefficients,fmrsfit-method"
      ]
    },
    {
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        "dispersion,dispersion-method",
        "dispersion,fmrsfit-method"
      ]
    },
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        "fitted,fitted-method",
        "fitted,fmrsfit-method"
      ]
    },
    {
      "page": "fmrs.gendata-methods",
      "title": "fmrs.gendata method",
      "concept": [
        "fmr, aft, censoring, data generation",
        "lnorm, norm, weibull"
      ],
      "topics": [
        "fmrs.gendata",
        "fmrs.gendata,ANY-method",
        "fmrs.gendata-method"
      ]
    },
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      "title": "fmrs.mle method",
      "concept": [
        "fmr, aft, mle, ridge, fmrs",
        "lnorm, norm, weibull"
      ],
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        "fmrs.mle,ANY-method",
        "fmrs.mle-method"
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    },
    {
      "page": "fmrs.tunsel-methods",
      "title": "fmrs.tunsel method",
      "concept": [
        "fmr, aft, lasso, adplasso, mcp, scad, sica, ridge",
        "lnorm, norm, weibull"
      ],
      "topics": [
        "fmrs.tunsel",
        "fmrs.tunsel,ANY-method",
        "fmrs.tunsel-method"
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    },
    {
      "page": "fmrs.varsel-methods",
      "title": "fmrs.varsel method",
      "concept": [
        "fmr, aft, lasso, adplasso, mcp, scad, sica, ridge, elastic net",
        "lnorm, norm, weibull"
      ],
      "topics": [
        "fmrs.varsel",
        "fmrs.varsel,ANY-method",
        "fmrs.varsel-method"
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    },
    {
      "page": "fmrsfit-class",
      "title": "An S4 class to represent a fitted FMRs model",
      "topics": [
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        "frmsfit"
      ]
    },
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      "topics": [
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        "logLik,fmrsfit-method",
        "logLik,logLik-method"
      ]
    },
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        "mixProp,fmrsfit-method",
        "mixProp,mixProp-method"
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        "ncov,fmrsfit-method",
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