{ "_id": "6a1e9b7e1d7bb097a0a6e994", "Package": "ungroup", "Type": "Package", "Title": "Penalized Composite Link Model for Efficient Estimation of\nSmooth Distributions from Coarsely Binned Data", "Version": "1.4.5", "Authors@R": "c(\nperson(\"Marius D.\", \"Pascariu\", role = c(\"aut\", \"cre\"), email = \"rpascariu@outlook.com\", comment = c(ORCID = \"0000-0002-2568-6489\")),\nperson(\"Silvia\", \"Rizzi\", role = \"aut\", email = \"srizzi@health.sdu.dk\"),\nperson(\"Jonas\", \"Schoeley\", role = \"aut\", comment = c(ORCID = \"0000-0002-3340-8518\")),\nperson(\"Maciej J.\", \"Danko\", role = \"aut\", email = \"Danko@demogr.mpg.de\", comment = c(ORCID = \"0000-0002-7924-9022\"))\n)", "Description": "Versatile method for ungrouping histograms (binned count\ndata) assuming that counts are Poisson distributed and that the\nunderlying sequence on a fine grid to be estimated is smooth.\nThe method is based on the composite link model and estimation\nis achieved by maximizing a penalized likelihood. Smooth\ndetailed sequences of counts and rates are so estimated from\nthe binned counts. Ungrouping binned data can be desirable for\nmany reasons: Bins can be too coarse to allow for accurate\nanalysis; comparisons can be hindered when different grouping\napproaches are used in different histograms; and the last\ninterval is often wide and open-ended and, thus, covers a lot\nof information in the tail area. Age-at-death distributions\ngrouped in age classes and abridged life tables are examples of\nbinned data. Because of modest assumptions, the approach is\nsuitable for many demographic and epidemiological applications.\nFor a detailed description of the method and applications see\nRizzi et al. 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