{ "_id": "6a1f069cb401979e7341bc54", "Package": "Q2q", "Type": "Package", "Title": "Interpolating Age-Specific Mortality Rates at All Ages", "Version": "0.1.1", "Authors@R": "person('Farid', 'FLICI', email = 'farid.flici@cread.dz', role = c('aut', 'cre'), comment = c(ORCID = 'https://orcid.org/0000-0002-1954-2120'))", "Description": "Mortality rates are typically provided in an abridged\nformat, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]',\nand so on. Some applications necessitate a detailed (single)\nage description. Despite the large number of proposed\napproaches in the literature, only a few methods ensure great\nperformance at both younger and higher ages. For example, the\n6-term 'Lagrange' interpolation function is well suited to\nmortality interpolation at younger ages (with irregular\nintervals), but not at older ages. The 'Karup-King' method, on\nthe other hand, performs well at older ages but is not suitable\nfor younger ones. Interested readers can find a full discussion\nof the two stated methods in the book Shryock, Siegel, and\nAssociates (1993).The Q2q package combines the two methods to\nallow for the interpolation of mortality rates across all age\ngroups. It begins by implementing each method independently,\nand then the resulting curves are linked using a 5-age averaged\nerror between the two partial curves.", "License": "GPL (>= 2.0)", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "URL": "https://github.com/Farid-FLICI/Q2q", "Repository": "https://farid-flici.r-universe.dev", "Date/Publication": "2024-12-19 15:58:20 UTC", "RemoteUrl": "https://github.com/farid-flici/q2q", "RemoteRef": "HEAD", "RemoteSha": "5abcc0d77113fe2b98180c70ffeb7b9476108077", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-18 07:01:49 UTC", "User": "root" }, "Author": "Farid FLICI [aut, cre] (ORCID: )", "Maintainer": "Farid FLICI ", "MD5sum": "5e64beffa995dad9ecc6800379336fd6", "_user": "farid-flici", "_type": "src", "_file": "Q2q_0.1.1.tar.gz", "_fileid": "14407a42e5805d0e0a3e0751251e5f960b0b3d1ffd09ea8a2b9102914967989d", "_filesize": 71145, "_sha256": "14407a42e5805d0e0a3e0751251e5f960b0b3d1ffd09ea8a2b9102914967989d", 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