{
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  "Package": "degross",
  "Title": "Density Estimation from GROuped Summary Statistics",
  "Version": "0.9.0",
  "Authors@R": "person(given = \"Philippe\",\nfamily = \"Lambert\",\nrole = c(\"aut\", \"cre\"),\nemail = \"p.lambert@uliege.be\",\ncomment = \"Université de Liège / Université catholique de Louvain (Belgium)\")",
  "Description": "Estimation of a density from grouped (tabulated) summary\nstatistics evaluated in each of the big bins (or classes)\npartitioning the support of the variable. These statistics\ninclude class frequencies and central moments of order one up\nto four. The log-density is modelled using a linear combination\nof penalised B-splines. The multinomial log-likelihood\ninvolving the frequencies adds up to a roughness penalty based\non the differences in the coefficients of neighbouring\nB-splines and the log of a root-n approximation of the sampling\ndensity of the observed vector of central moments in each\nclass. The so-obtained penalized log-likelihood is maximized\nusing the EM algorithm to get an estimate of the spline\nparameters and, consequently, of the variable density and\nrelated quantities such as quantiles, see Lambert, P. (2021)\n<arXiv:2107.03883> for details.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.1.1",
  "Repository": "https://plambertuliege.r-universe.dev",
  "Date/Publication": "2023-03-26 09:44:28 UTC",
  "RemoteUrl": "https://github.com/plambertuliege/degross",
  "RemoteRef": "HEAD",
  "RemoteSha": "4fb733597a7f56c12351b7276eb6a3333b93a9f0",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 06:22:27 UTC",
    "User": "root"
  },
  "Author": "Philippe Lambert [aut, cre] (Université de Liège / Université\ncatholique de Louvain (Belgium))",
  "Maintainer": "Philippe Lambert <p.lambert@uliege.be>",
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  "_published": "2026-06-02T14:08:58.877Z",
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    "message": "README updated\n",
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  "_maintainer": {
    "name": "Philippe Lambert",
    "email": "p.lambert@uliege.be",
    "login": "plambertuliege",
    "description": "Professor at ULiege & UCLouvain\n",
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    "degross",
    "degross_lpost",
    "degross_lpostBasic",
    "degrossData",
    "pdegross",
    "qdegross",
    "Sigma_fun",
    "simDegrossData"
  ],
  "_help": [
    {
      "page": "ddegross",
      "title": "Density function based on an object resulting from the estimation procedure in degross.",
      "topics": [
        "ddegross"
      ]
    },
    {
      "page": "degross",
      "title": "Density estimation from tabulated data with given frequencies and group central moments.",
      "topics": [
        "degross"
      ]
    },
    {
      "page": "degross_lpost",
      "title": "Log-posterior (with gradient and Fisher information) for given spline parameters, small bin frequencies, tabulated sample moments and roughness penalty parameter. This function is maximized during the M-step of the EM algorithm to estimate the B-spline parameters entering the density specification.",
      "topics": [
        "degross_lpost"
      ]
    },
    {
      "page": "degross_lpostBasic",
      "title": "Log-posterior for given spline parameters, big bin (and optional: small bin) frequencies, tabulated sample moments and roughness penalty parameter. Compared to degross_lpost, no Fisher information matrix is computed and the gradient evaluation is optional, with a resulting computational gain.",
      "topics": [
        "degross_lpostBasic"
      ]
    },
    {
      "page": "degross.object",
      "title": "Object resulting from the estimation of a density from grouped (tabulated) summary statistics",
      "topics": [
        "degross.object"
      ]
    },
    {
      "page": "degrossData",
      "title": "Creates a degrossData.object from the observed tabulated frequencies and central moments.",
      "topics": [
        "degrossData"
      ]
    },
    {
      "page": "degrossData.object",
      "title": "Object generated from grouped summary statistics, including tabulated frequencies and central moments of order 1 up to 4, to estimate the underlying density using 'degross'.",
      "topics": [
        "degrossData.object"
      ]
    },
    {
      "page": "pdegross",
      "title": "Cumulative distribution function (cdf) based on an object resulting from the estimation procedure in degross.",
      "topics": [
        "pdegross"
      ]
    },
    {
      "page": "plot.degross",
      "title": "Plot the density estimate obtained from grouped summary statistics using degross and superpose it to the observed histogram.",
      "topics": [
        "plot.degross"
      ]
    },
    {
      "page": "print.degross",
      "title": "Print a 'degross' object.",
      "topics": [
        "print.degross"
      ]
    },
    {
      "page": "print.degrossData",
      "title": "Print a 'degrossData' object.",
      "topics": [
        "print.degrossData"
      ]
    },
    {
      "page": "qdegross",
      "title": "Quantile function based on an object resulting from the estimation procedure in degross.",
      "topics": [
        "qdegross"
      ]
    },
    {
      "page": "Sigma_fun",
      "title": "Variance-covariance of sample central moments (root-n approximation) given the vector mu with the theoretical moments of order 1 to 8. CAREFUL: the result must be divided by n (= sample size)!",
      "topics": [
        "Sigma_fun"
      ]
    },
    {
      "page": "simDegrossData",
      "title": "Simulation of grouped data and their sample moments to illustrate the degross density estimation procedure",
      "topics": [
        "simDegrossData"
      ]
    }
  ],
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