{
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  "Package": "mixtools",
  "Version": "2.0.0",
  "Date": "2022-12-04",
  "Title": "Tools for Analyzing Finite Mixture Models",
  "Authors@R": "c(person(\"Derek\", \"Young\", role = c(\"aut\", \"cre\"),\nemail = \"derek.young@uky.edu\", comment = c(ORCID = \"0000-0002-3048-3803\")),\nperson(\"Tatiana\", \"Benaglia\", role = \"aut\"),\nperson(\"Didier\", \"Chauveau\", role = \"aut\"),\nperson(\"David\", \"Hunter\", role = \"aut\"),\nperson(\"Kedai\", \"Cheng\", role = \"aut\"),\nperson(\"Ryan\", \"Elmore\", role = \"ctb\"),\nperson(\"Thomas\", \"Hettmansperger\", role = \"ctb\"),\nperson(\"Hoben\", \"Thomas\", role = \"ctb\"),\nperson(\"Fengjuan\", \"Xuan\", role = \"ctb\"))",
  "URL": "https://github.com/dsy109/mixtools",
  "Description": "Analyzes finite mixture models for various parametric and\nsemiparametric settings.  This includes mixtures of parametric\ndistributions (normal, multivariate normal, multinomial,\ngamma), various Reliability Mixture Models (RMMs),\nmixtures-of-regressions settings (linear regression, logistic\nregression, Poisson regression, linear regression with\nchangepoints, predictor-dependent mixing proportions, random\neffects regressions, hierarchical mixtures-of-experts), and\ntools for selecting the number of components (bootstrapping the\nlikelihood ratio test statistic, mixturegrams, and model\nselection criteria).  Bayesian estimation of\nmixtures-of-linear-regressions models is available as well as a\nnovel data depth method for obtaining credible bands.  This\npackage is based upon work supported by the National Science\nFoundation under Grant No. SES-0518772 and the Chan Zuckerberg\nInitiative: Essential Open Source Software for Science (Grant\nNo. 2020-255193).",
  "License": "GPL (>= 2)",
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  "Repository": "https://dsy109.r-universe.dev",
  "Date/Publication": "2024-06-17 14:12:59 UTC",
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    "ddirichlet",
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    "density.spEM",
    "depth",
    "dexpmixt",
    "dmvnorm",
    "ellipse",
    "expRMM_EM",
    "flaremix.init",
    "flaremixEM",
    "gammamix.init",
    "gammamixEM",
    "hmeEM",
    "ise.npEM",
    "lambda",
    "lambda.pert",
    "ldmult",
    "logdmvnorm",
    "logisregmix.init",
    "logisregmixEM",
    "makemultdata",
    "matsqrt",
    "mixturegram",
    "multmix.init",
    "multmixEM",
    "multmixmodel.sel",
    "mvnormalmix.init",
    "mvnormalmixEM",
    "mvnpEM",
    "normalmix.init",
    "normalmixEM",
    "normalmixEM2comp",
    "normalmixMMlc",
    "normmix.sim",
    "normmixrm.sim",
    "npEM",
    "npEMindrep",
    "npEMindrepbw",
    "npMSL",
    "parse.constraints",
    "perm",
    "plot.mixEM",
    "plot.mixMCMC",
    "plot.mvnpEM",
    "plot.npEM",
    "plot.spEM",
    "plot.spEMN01",
    "plotexpRMM",
    "plotFDR",
    "plotly_compCDF",
    "plotly_ellipse",
    "plotly_expRMM",
    "plotly_FDR",
    "plotly_ise.npEM",
    "plotly_mixEM",
    "plotly_mixMCMC",
    "plotly_mixturegram",
    "plotly_npEM",
    "plotly_post.beta",
    "plotly_seq.npEM",
    "plotly_spEMN01",
    "plotly_spRMM",
    "plotly_weibullRMM",
    "plotseq",
    "plotseq.npEM",
    "plotspRMM",
    "plotweibullRMM",
    "poisregmix.init",
    "poisregmixEM",
    "post.beta",
    "print.mvnpEM",
    "print.npEM",
    "print.summary.mvnpEM",
    "print.summary.npEM",
    "regcr",
    "regmix.init",
    "regmix.lambda.init",
    "regmix.mixed.init",
    "regmixEM",
    "regmixEM.lambda",
    "regmixEM.loc",
    "regmixEM.mixed",
    "regmixMH",
    "regmixmodel.sel",
    "repnormmix.init",
    "repnormmixEM",
    "repnormmixmodel.sel",
    "rexpmix",
    "rlnormscalemix",
    "rmvnorm",
    "rmvnormmix",
    "rnormmix",
    "rweibullmix",
    "segregmix.init",
    "segregmixEM",
    "spEM",
    "spEMsymloc",
    "spEMsymlocN01",
    "spregmix",
    "spRMM_SEM",
    "summary.mixEM",
    "summary.mvnpEM",
    "summary.npEM",
    "summary.spRMM",
    "tauequivnormalmixEM",
    "test.equality",
    "test.equality.mixed",
    "try.flare",
    "weibullRMM_SEM",
    "wIQR",
    "wkde",
    "wquantile"
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      "title": "GNP and CO2 Data Set",
      "object": "CO2data",
      "file": "CO2data.RData",
      "class": [
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  "_help": [
    {
      "page": "boot.comp",
      "title": "Performs Parametric Bootstrap for Sequentially Testing the Number of Components in Various Mixture Models",
      "topics": [
        "boot.comp"
      ]
    },
    {
      "page": "boot.se",
      "title": "Performs Parametric Bootstrap for Standard Error Approximation",
      "topics": [
        "boot.se"
      ]
    },
    {
      "page": "CO2data",
      "title": "GNP and CO2 Data Set",
      "topics": [
        "CO2data"
      ]
    },
    {
      "page": "compCDF",
      "title": "Plot the Component CDF",
      "topics": [
        "compCDF"
      ]
    },
    {
      "page": "density.npEM",
      "title": "Normal kernel density estimate for nonparametric EM output",
      "topics": [
        "density.npEM"
      ]
    },
    {
      "page": "density.spEM",
      "title": "Normal kernel density estimate for semiparametric EM output",
      "topics": [
        "density.spEM"
      ]
    },
    {
      "page": "depth",
      "title": "Elliptical and Spherical Depth",
      "topics": [
        "depth"
      ]
    },
    {
      "page": "dmvnorm",
      "title": "The Multivariate Normal Density",
      "topics": [
        "dmvnorm",
        "logdmvnorm"
      ]
    },
    {
      "page": "ellipse",
      "title": "Draw Two-Dimensional Ellipse Based on Mean and Covariance",
      "topics": [
        "ellipse"
      ]
    },
    {
      "page": "expRMM_EM",
      "title": "EM algorithm for Reliability Mixture Models (RMM) with right Censoring",
      "topics": [
        "expRMM_EM"
      ]
    },
    {
      "page": "flaremixEM",
      "title": "EM Algorithm for Mixtures of Regressions with Flare",
      "topics": [
        "flaremixEM"
      ]
    },
    {
      "page": "gammamixEM",
      "title": "EM Algorithm for Mixtures of Gamma Distributions",
      "topics": [
        "gammamixEM"
      ]
    },
    {
      "page": "Habituationdata",
      "title": "Infant habituation data",
      "topics": [
        "Habituationdata"
      ]
    },
    {
      "page": "hmeEM",
      "title": "EM Algorithm for Mixtures-of-Experts",
      "topics": [
        "hmeEM"
      ]
    },
    {
      "page": "ise.npEM",
      "title": "Integrated Squared Error for a selected density from npEM output",
      "topics": [
        "ise.npEM"
      ]
    },
    {
      "page": "logisregmixEM",
      "title": "EM Algorithm for Mixtures of Logistic Regressions",
      "topics": [
        "logisregmixEM"
      ]
    },
    {
      "page": "makemultdata",
      "title": "Produce Cutpoint Multinomial Data",
      "topics": [
        "makemultdata"
      ]
    },
    {
      "page": "mixturegram",
      "title": "Mixturegrams",
      "topics": [
        "mixturegram"
      ]
    },
    {
      "page": "multmixEM",
      "title": "EM Algorithm for Mixtures of Multinomials",
      "topics": [
        "multmixEM"
      ]
    },
    {
      "page": "multmixmodel.sel",
      "title": "Model Selection Mixtures of Multinomials",
      "topics": [
        "multmixmodel.sel"
      ]
    },
    {
      "page": "mvnormalmixEM",
      "title": "EM Algorithm for Mixtures of Multivariate Normals",
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