{ "_id": "6a1eb77cb25058d4daade7c4", "Package": "picR", "Title": "Predictive Information Criteria for Model Selection", "Version": "1.0.0", "Authors@R": "person(\"Javier\", \"Flores\", , \"javenrflo.pro@pm.me\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-1550-1655\"))", "Description": "Computation of predictive information criteria (PIC) from\nselect model object classes for model selection in predictive\ncontexts. In contrast to the more widely used Akaike\nInformation Criterion (AIC), which are derived under the\nassumption that target(s) of prediction (i.e. validation data)\nare independently and identically distributed to the fitting\ndata, the PIC are derived under less restrictive assumptions\nand thus generalize AIC to the more practically relevant case\nof training/validation data heterogeneity. The methodology\nfeatured in this package is based on Flores (2021)\n\n\"A new class of information criteria for improved prediction in\nthe presence of training/validation data heterogeneity\".", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.0", "Config/testthat/edition": "3", "URL": "https://github.com/javenrflo/picR", "BugReports": "https://github.com/javenrflo/picR/issues", "Repository": "https://javenrflo.r-universe.dev", "Date/Publication": "2022-10-21 20:03:34 UTC", "RemoteUrl": "https://github.com/javenrflo/picr", "RemoteRef": "HEAD", "RemoteSha": "86937daf4d62927018bfa78a756cd4a380af9781", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 10:49:31 UTC", "User": "root" }, "Author": "Javier Flores [aut, cre] (ORCID:\n)", "Maintainer": "Javier Flores ", "MD5sum": "09326d84c736228f2bad1c29f5bfe82a", "_user": "javenrflo", "_type": "src", "_file": "picR_1.0.0.tar.gz", "_fileid": 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