{ "_id": "6a1001d2acfb0bcc41c7bf5c", "Package": "FMCensSkewReg", "Title": "Finite Mixture of Censored Regression Models with Skewed\nDistributions", "Version": "0.1.1", "Authors@R": "c(\nperson(\"Jiwon\", \"Park\", email = \"pcjylove87@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Victor Hugo\", \"Lachos Davila\", email = \"hlachos@uconn.edu\", role = \"aut\"),\nperson(\"Dipak\", \"Dey\", email = \"dipak.dey@uconn.edu\", role = \"aut\")\n)", "Author": "Jiwon Park [aut, cre], Victor Hugo Lachos Davila [aut], Dipak\nDey [aut]", "Maintainer": "Jiwon Park ", "Description": "Provides an implementation of finite mixture regression\nmodels for censored data under four distributional families:\nNormal (FM-NCR), Student t (FM-TCR), skew-Normal (FM-SNCR), and\nskew-t (FM-STCR). The package enables flexible modeling of\nskewness and heavy tails often observed in real-world data,\nwhile explicitly accounting for censoring. Functions are\nincluded for parameter estimation via the\nExpectation-Maximization (EM) algorithm, computation of\nstandard errors, and model comparison criteria such as the\nAkaike Information Criterion (AIC), the Bayesian Information\nCriterion (BIC), and the Efficient Determination Criterion\n(EDC). The underlying methodology is described in Park et al.\n(2024) .", "License": "MIT + file LICENSE", "URL": "https://github.com/JiwonPark41/FMCensSkewReg", "BugReports": "https://github.com/JiwonPark41/FMCensSkewReg/issues", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "Config/pak/sysreqs": "pari-gp", "Repository": "https://jiwonpark41.r-universe.dev", "Date/Publication": "2025-12-17 14:28:06 UTC", "RemoteUrl": "https://github.com/jiwonpark41/fmcensskewreg", "RemoteRef": "HEAD", "RemoteSha": "d33d876ac48af4ab2a900d278a2431aca319cf38", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 07:09:04 UTC", "User": "root" }, "MD5sum": "bb34ea3889aa9ba21a8fe189495bacd3", "_user": "jiwonpark41", "_type": "src", "_file": "FMCensSkewReg_0.1.1.tar.gz", "_fileid": "e81098cd8c1be99a4dec14f49e1f097f63d9344c32123f52ca850e87936be764", "_filesize": 127160, "_sha256": 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