{ "_id": "6ad9cc5f021a938ed758eaa9", "Package": "MGLM", "Version": "0.2.3", "Date": "2026-08-23", "Title": "Multivariate Response Generalized Linear Models", "Authors@R": "c(\nperson(\"Yiwen\", \"Zhang\", email = \"zhangyiwen1015@gmail.com\", role = c(\"aut\")),\nperson(\"Hua\", \"Zhou\", email = \"huazhou@ucla.edu\", role = c(\"aut\")),\nperson(\"Juhyun\", \"Kim\", email = \"juhkim111@ucla.edu\", role = c(\"cre\")),\nperson(\"Magdalena\", \"Czapiewska\", role = c(\"ctb\"),\ncomment = c(ORCID = \"0009-0004-3265-1614\"))\n)", "Description": "Provides functions that (1) fit multivariate discrete\ndistributions, (2) generate random numbers from multivariate\ndiscrete distributions, and (3) run regression and penalized\nregression on the multivariate categorical response data.\nImplemented models include: multinomial logit model, Dirichlet\nmultinomial model, generalized Dirichlet multinomial model, and\nnegative multinomial model. Making the best of the\nminorization-maximization (MM) algorithm and Newton-Raphson\nmethod, we derive and implement stable and efficient algorithms\nto find the maximum likelihood estimates. On a multi-core\nmachine, multi-threading is supported.", "VignetteBuilder": "knitr", "LazyLoad": "yes", "LazyData": "yes", "License": "GPL (>= 2)", "RoxygenNote": "7.1.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-22 08:38:07 UTC", "User": "root" }, "Author": "Yiwen Zhang [aut], Hua Zhou [aut], Juhyun Kim [cre], Magdalena\nCzapiewska [ctb] (ORCID:\n)", "Maintainer": "Juhyun Kim ", "Repository": "https://juhyun-kim-ucla.r-universe.dev", "Date/Publication": "2026-08-23 20:43:43 UTC", "RemoteUrl": "https://github.com/cran/MGLM", "RemoteRef": "HEAD", "RemoteSha": "2d3d14956f2e0beaa6f5d403b20d42723adb1254", "_user": "juhyun-kim-ucla", "_type": "src", "_file": "MGLM_0.2.3.tar.gz", "_fileid": "https://r2.ropensci.org/ff8898f30a5db4df7bbab10cd66001098b079647a2c63fe45f9f1ca2034e82bf", "_filesize": 380899, "_sha256": "ff8898f30a5db4df7bbab10cd66001098b079647a2c63fe45f9f1ca2034e82bf", "_expires": "2027-01-30T08:42:05.000Z", "_created": "2026-10-22T08:38:07.000Z", "_published": 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