{ "_id": "6ab7943199f7aa8c6aa3271e", "Package": "cdlogit", "Title": "Context-Dependent Discrete Choice Models", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Yuan\", \"Xi\", , \"yuan.xi@kuleuven.be\", role = c(\"aut\", \"cre\")),\nperson(\"Michel\", \"Meulders\", , \"michel.meulders@kuleuven.be\", role = \"ctb\"),\nperson(\"Martina\", \"Vandebroek\", , \"martina.vandebroek@kuleuven.be\", role = \"ctb\")\n)", "Description": "Implements a class of context-dependent discrete choice\nmodels, including the standard random utility maximization\n(RUM) model, classical and generalized random regret\nminimization (RRM) models, the random advantage maximization\n(RAM) model, as well as the Emergent Value (EV) model and the\ncontextual random utility maximization (CRUM) model, which\nexplicitly incorporate measures of context dependence. In\naddition, pairwise normalization (PN) and range normalization\n(RN) models are supported. Both fixed-parameter and\nrandom-parameter specifications can be estimated, the latter\nallowing for preference heterogeneity. All models are\nformulated within a logit framework under the assumption of\nindependently and identically distributed (i.i.d.) Gumbel error\nterms. The implemented methods are based on Tversky and\nSimonson (1993) , Chorus et al.\n(2014) , Rooderkerk et al.\n(2011) , Guevara and Fukushi (2016)\n, Landry and Webb (2021)\n, and Daviet and Webb (2023)\n.", "Author": "Yuan Xi [aut, cre], Michel Meulders [ctb], Martina Vandebroek\n[ctb]", "Maintainer": "Yuan Xi ", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-26 09:42:15 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-28 16:30:15 UTC", "RemoteUrl": "https://github.com/cran/cdlogit", "RemoteRef": "HEAD", "RemoteSha": "c6473b78cc1bc116476606e559c3329a95443658", "_user": "cran", "_type": "src", "_file": "cdlogit_1.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/b7e0fb9cf7987320ac577511d7ec4a8ed7c2f54bbc48fe16192c247cad3965c1", "_filesize": 179382, "_sha256": "b7e0fb9cf7987320ac577511d7ec4a8ed7c2f54bbc48fe16192c247cad3965c1", "_expires": 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