{ "_id": "6a1ef2ebb401979e73414ee4", "Package": "GLDreg", "Type": "Package", "Title": "Fit GLD Regression/Quantile/AFT Model to Data", "LazyData": "yes", "Version": "1.1.2", "Date": "2025-07-23", "Authors@R": "c(person(\"Steve\", \"Su\", role = c(\"aut\", \"cre\",\"cph\"), email = \"allegro.su@gmail.com\", comment=c(ORCID = \"0000-0002-3368-4926\")),person(\"R\", \"Core Team\", role = \"aut\"))", "Maintainer": "Steve Su ", "Description": "Owing to the rich shapes of Generalised Lambda\nDistributions (GLDs), GLD standard/quantile/Accelerated Failure\nTime (AFT) regression is a competitive flexible model compared\nto standard/quantile/AFT regression. The proposed method has\nsome major advantages: 1) it provides a reference line which is\nvery robust to outliers with the attractive property of zero\nmean residuals and 2) it gives a unified, elegant quantile\nregression model from the reference line with smooth regression\ncoefficients across different quantiles. For AFT model, it also\neliminates the needs to try several different AFT models, owing\nto the flexible shapes of GLD. The goodness of fit of the\nproposed model can be assessed via QQ plots and\nKolmogorov-Smirnov tests and data driven smooth test, to ensure\nthe appropriateness of the statistical inference under\nconsideration. Statistical distributions of coefficients of the\nGLD regression line are obtained using simulation, and interval\nestimates are obtained directly from simulated data. References\ninclude the following: Su (2015) \"Flexible Parametric Quantile\nRegression Model\" , Su (2021)\n\"Flexible parametric accelerated failure time\nmodel\".", "License": "GPL (>= 3)", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 07:39:05 UTC", "User": "root" }, "Author": "Steve Su [aut, cre, cph] (ORCID:\n), R Core Team [aut]", "Repository": "https://allegropiano.r-universe.dev", "Date/Publication": "2025-07-23 08:10:07 UTC", "RemoteUrl": "https://github.com/cran/GLDreg", "RemoteRef": "HEAD", "RemoteSha": "fd8169db92c320b0e6ff7c62a63e3bb41108d2c3", "MD5sum": "8850fd3d50c541a603901563ff69390c", "_user": "allegropiano", "_type": "src", "_file": "GLDreg_1.1.2.tar.gz", "_fileid": "402e77f8e0e988f344a6c5a8d7d4d16e2dbd803c4e0031f79e5bc5792605a73d", "_filesize": 188290, "_sha256": "402e77f8e0e988f344a6c5a8d7d4d16e2dbd803c4e0031f79e5bc5792605a73d", "_created": "2026-05-19T07:39:05.000Z", "_published": "2026-06-02T15:12:43.023Z", "_distro": "noble", "_jobs": [ { "job": 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