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  "Title": "Variable Selection in Finite Mixture of AFT Regression and FMR\nModels",
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  "Authors@R": "c(person(\"Farhad\", \"Shokoohi\", role = c(\"aut\", \"cre\"),\nemail = \"shokoohi@icloud.com\", comment = c(ORCID = \"0000-0002-6224-2609\"))\n)",
  "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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  "Author": "Farhad Shokoohi [aut, cre]\n(<https://orcid.org/0000-0002-6224-2609>)",
  "Maintainer": "Farhad Shokoohi <shokoohi@icloud.com>",
  "Repository": "https://bioc-release.r-universe.dev",
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    "author": "A Wokaty <andres.wokaty@sph.cuny.edu>",
    "committer": "A Wokaty <andres.wokaty@sph.cuny.edu>",
    "message": "bump x.y.z version to even y prior to creation of RELEASE_3_23 branch\n",
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    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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    "extra/citation.html",
    "extra/citation.json",
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    "coefficients",
    "dispersion",
    "fitted",
    "fmrs.gendata",
    "fmrs.mle",
    "fmrs.tunsel",
    "fmrs.varsel",
    "fmrstunpar",
    "logLik",
    "mixProp",
    "ncomp",
    "ncov",
    "nobs",
    "residuals",
    "summary",
    "weights"
  ],
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    {
      "page": "fmrs-package",
      "title": "Variable Selection in Finite Mixture of AFT Regression and FMR Models",
      "topics": [
        "fmrs-package",
        "fmrs"
      ]
    },
    {
      "page": "BIC-methods",
      "title": "BIC method",
      "topics": [
        "BIC",
        "BIC,BIC-method",
        "BIC,fmrsfit-method"
      ]
    },
    {
      "page": "coefficients-methods",
      "title": "coefficients method",
      "topics": [
        "coefficients",
        "coefficients,coefficients-method",
        "coefficients,fmrsfit-method"
      ]
    },
    {
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      "title": "dispersion method",
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        "dispersion,dispersion-method",
        "dispersion,fmrsfit-method"
      ]
    },
    {
      "page": "fitted-methods",
      "title": "fitted method",
      "topics": [
        "fitted",
        "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"
      ]
    },
    {
      "page": "fmrs.mle-methods",
      "title": "fmrs.mle method",
      "concept": [
        "fmr, aft, mle, ridge, fmrs",
        "lnorm, norm, weibull"
      ],
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        "fmrs.mle",
        "fmrs.mle,ANY-method",
        "fmrs.mle-method"
      ]
    },
    {
      "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"
      ]
    },
    {
      "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"
      ]
    },
    {
      "page": "fmrsfit-class",
      "title": "An S4 class to represent a fitted FMRs model",
      "topics": [
        "fmrsfit-class",
        "frmsfit"
      ]
    },
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      "page": "fmrstunpar-class",
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      "topics": [
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        "fmrstunpar-class"
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    },
    {
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      "topics": [
        "logLik",
        "logLik,fmrsfit-method",
        "logLik,logLik-method"
      ]
    },
    {
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      "topics": [
        "mixProp",
        "mixProp,fmrsfit-method",
        "mixProp,mixProp-method"
      ]
    },
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        "ncomp",
        "ncomp,fmrsfit-method",
        "ncomp,ncomp-method"
      ]
    },
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        "ncov,fmrsfit-method",
        "ncov,ncov-method"
      ]
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        "nobs,fmrsfit-method",
        "nobs,nobs-method"
      ]
    },
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      "topics": [
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        "residuals,fmrsfit-method",
        "residuals,residuals-method"
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        "summary,fmrsfit-method",
        "summary,fmrstunpar-method",
        "summary,summary-method"
      ]
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      ]
    }
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