{ "_id": "6ac4ed77f511ae58c09a329f", "Package": "GLDEX", "Version": "2.0.0.9.4", "Date": "2025-07-23", "Title": "Fitting Single and Mixture of Generalised Lambda Distributions", "Authors@R": "c(person(\"Steve\", \"Su\", role = c(\"aut\", \"cre\",\"cph\"),\nemail = \"allegro.su@gmail.com\", comment=c(ORCID = \"0000-0002-3368-4926\")),\nperson(\"Martin\", \"Maechler\", role = \"aut\"),\nperson(\"Juha\", \"Karvanen\", role = \"aut\"),\nperson(\"Robert\", \"King\", role = \"aut\"),\nperson(\"Benjamin\", \"Dean\", role = \"ctb\"),\nperson(\"R\", \"Core Team\", role = \"aut\"))", "Maintainer": "Steve Su ", "Description": "The fitting algorithms considered in this package have two\nmajor objectives. One is to provide a smoothing device to fit\ndistributions to data using the weight and unweighted\ndiscretised approach based on the bin width of the histogram.\nThe other is to provide a definitive fit to the data set using\nthe maximum likelihood and quantile matching estimation. Other\nmethods such as moment matching, starship method, L moment\nmatching are also provided. Diagnostics on goodness of fit can\nbe done via qqplots, KS-resample tests and comparing mean,\nvariance, skewness and kurtosis of the data with the fitted\ndistribution. References include the following: Karvanen and\nNuutinen (2008) \"Characterizing the generalized lambda\ndistribution by L-moments\" ,\nKing and MacGillivray (1999) \"A starship method for fitting the\ngeneralised lambda distributions\"\n, Su (2005) \"A Discretized\nApproach to Flexibly Fit Generalized Lambda Distributions to\nData\" , Su (2007) \"Nmerical\nMaximum Log Likelihood Estimation for Generalized Lambda\nDistributions\" , Su (2007)\n\"Fitting Single and Mixture of Generalized Lambda Distributions\nto Data via Discretized and Maximum Likelihood Methods: GLDEX\nin R\" , Su (2009) \"Confidence\nIntervals for Quantiles Using Generalized Lambda Distributions\"\n, Su (2010) \"Chapter 14:\nFitting GLDs and Mixture of GLDs to Data using Quantile\nMatching Method\" , Su (2010) \"Chapter 15:\nFitting GLD to data using GLDEX 1.0.4 in R\"\n, Su (2015) \"Flexible Parametric Quantile\nRegression Model\" , Su (2021)\n\"Flexible parametric accelerated failure time\nmodel\".", "License": "GPL (>= 3)", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-06 12:39:06 UTC", "User": "root" }, "Author": "Steve Su [aut, cre, cph] (ORCID:\n), Martin Maechler\n[aut], Juha Karvanen [aut], Robert King [aut], Benjamin Dean\n[ctb], R Core Team [aut]", "Repository": "https://allegropiano.r-universe.dev", "Date/Publication": "2025-07-23 08:30:10 UTC", "RemoteUrl": "https://github.com/cran/GLDEX", "RemoteRef": "HEAD", "RemoteSha": "66b26f89179354a612b31aad5eab8d6c2bc13a52", "_user": "allegropiano", "_type": "src", "_file": "GLDEX_2.0.0.9.4.tar.gz", "_fileid": "https://r2.ropensci.org/00c42e521f27be2f939a9eea0c8ef250226edd784734d64d5cd2f6806d101e07", "_filesize": 514449, "_sha256": "00c42e521f27be2f939a9eea0c8ef250226edd784734d64d5cd2f6806d101e07", "_expires": "2027-01-14T12:45:40.000Z", "_created": "2026-10-06T12:39:06.000Z", "_published": "2026-10-06T12:45:43.777Z", "_jobs": [ { "job": 112273169874, "time": 78, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": 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It also provides functions for fitting bimodal distributions using mixtures of generalised lambda distributions.", "topics": [ "GLDEX-package", "GLDEX" ] }, { "page": "digitsBase", "title": "Digit/Bit Representation of Integers in any Base", "topics": [ "digitsBase" ] }, { "page": "fun.auto.bimodal.ml", "title": "Fitting mixture of generalied lambda distribtions to data using maximum likelihood estimation via the EM algorithm", "topics": [ "fun.auto.bimodal.ml" ] }, { "page": "fun.auto.bimodal.pml", "title": "Fitting mixture of generalied lambda distribtions to data using parition maximum likelihood estimation", "topics": [ "fun.auto.bimodal.pml" ] }, { "page": "fun.auto.bimodal.qs", "title": "Fitting mixtures of generalied lambda distribtions to data using quantile matching method", "topics": [ "fun.auto.bimodal.qs" ] }, { "page": "fun.bimodal.fit.ml", "title": "Finds the final fits using the maximum likelihood estimation for the bimodal dataset.", "topics": [ "fun.bimodal.fit.ml" ] }, { "page": "fun.bimodal.fit.pml", "title": "Finds the final fits using partition maximum likelihood estimation for the bimodal dataset.", "topics": [ "fun.bimodal.fit.pml" ] }, { "page": "fun.bimodal.init", "title": "Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.", "topics": [ "fun.bimodal.init" ] }, { "page": "fun.check.gld", "title": "Check whether the RS or FMKL/FKML GLD is a valid GLD for single values of L1, L2, L3 and L4", "topics": [ "fun.check.gld" ] }, { "page": "fun.check.gld.multi", "title": "Check whether the RS or FMKL/FKML GLD is a valid GLD for vectors of L1, L2, L3 and L4", "topics": [ "fun.check.gld.multi" ] }, { "page": "fun.class.regime.bi", "title": "Classifies data into two groups using a clustering regime.", "topics": [ "fun.class.regime.bi" ] }, { "page": "fun.comp.moments.ml", "title": "Compare the moments of the data and the fitted univariate generalised lambda distribution.", "topics": [ "fun.comp.moments.ml" ] }, { "page": "fun.comp.moments.ml.2", "title": "Compare the moments of the data and the fitted univariate generalised lambda distribution. Specialised funtion designed for RMFMKL.ML and STAR methods.", "topics": [ "fun.comp.moments.ml.2" ] }, { "page": "fun.data.fit.hs", "title": "Fit RS and FMKL generalised distributions to data using discretised approach with weights.", "topics": [ "fun.data.fit.hs" ] }, { "page": "fun.data.fit.hs.nw", "title": "Fit RS and FMKL generalised distributions to data using discretised approach without weights.", "topics": [ "fun.data.fit.hs.nw" ] }, { "page": "fun.data.fit.lm", "title": "Fit data using L moment matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.lm" ] }, { "page": "fun.data.fit.ml", "title": "Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.", "topics": [ "fun.data.fit.ml" ] }, { "page": "fun.data.fit.mm", "title": "Fit data using moment matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.mm" ] }, { "page": "fun.data.fit.qs", "title": "Fit data using quantile matching estimation for RS and FMKL GLD", "topics": [ "fun.data.fit.qs" ] }, { "page": "fun.diag.ks.g", "title": "Compute the simulated Kolmogorov-Smirnov tests for the unimodal dataset", "topics": [ "fun.diag.ks.g" ] }, { "page": "fun.diag.ks.g.bimodal", "title": "Compute the simulated Kolmogorov-Smirnov tests for the bimodal dataset", "topics": [ "fun.diag.ks.g.bimodal" ] }, { "page": "fun.diag1", "title": "Diagnostic function for theoretical distribution fits through the resample Kolmogorov-Smirnoff tests", "topics": [ "fun.diag1" ] }, { "page": "fun.diag2", "title": "Diagnostic function for empirical data distribution fits through the resample Kolmogorov-Smirnoff tests", "topics": [ "fun.diag2" ] }, { "page": "fun.disc.estimation", "title": "Estimates the mean and variance after cutting up a vector of variable into evenly spaced categories.", "topics": [ "fun.disc.estimation" ] }, { "page": "fun.gen.qrn", "title": "Finds the low discrepancy quasi random numbers", "topics": [ "fun.gen.qrn" ] }, { "page": "fun.lm.theo.gld", "title": 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lambda distribution fits on the data set.", "topics": [ "fun.plot.fit" ] }, { "page": "fun.plot.fit.bm", "title": "Plotting mixture of two generalised lambda distributions on the data set.", "topics": [ "fun.plot.fit.bm" ] }, { "page": "fun.plot.many.gld", "title": "Plotting many univariate generalised lambda distributions on one page.", "topics": [ "fun.plot.many.gld" ] }, { "page": "fun.rawmoments", "title": "Computes the raw moments of the generalised lambda distribution up to 4th order.", "topics": [ "fun.rawmoments" ] }, { "page": "fun.RMFMKL.hs", "title": "Fit FMKL generalised distribution to data using discretised approach with weights.", "topics": [ "fun.RMFMKL.hs" ] }, { "page": "fun.RMFMKL.hs.nw", "title": "Fit FMKL generalised distribution to data using discretised approach without weights.", "topics": [ "fun.RMFMKL.hs.nw" ] }, { "page": "fun.RMFMKL.lm", "title": "Fit FMKL generalised lambda distribution to data set using L moment matching", "topics": [ "fun.RMFMKL.lm" ] }, { "page": "fun.RMFMKL.ml", "title": "Fit FMKL generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RMFMKL.ml" ] }, { "page": "fun.RMFMKL.ml.m", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RMFMKL.ml.m" ] }, { "page": "fun.RMFMKL.mm", "title": "Fit FMKL generalised lambda distribution to data set using moment matching", "topics": [ "fun.RMFMKL.mm" ] }, { "page": "fun.RMFMKL.qs", "title": "Fit FMKL generalised lambda distribution to data set using quantile matching", "topics": [ "fun.RMFMKL.qs" ] }, { "page": "fun.RPRS.hs", "title": "Fit RS generalised distribution to data using discretised approach with weights.", "topics": [ "fun.RPRS.hs" ] }, { "page": "fun.RPRS.hs.nw", "title": "Fit RS generalised distribution to data using discretised approach without weights.", "topics": [ "fun.RPRS.hs.nw" ] }, { "page": "fun.RPRS.lm", "title": "Fit RS generalised lambda distribution to data set using L moment matching", "topics": [ "fun.RPRS.lm" ] }, { "page": "fun.RPRS.ml", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RPRS.ml" ] }, { "page": "fun.RPRS.ml.m", "title": "Fit RS generalised lambda distribution to data set using maximum likelihood estimation", "topics": [ "fun.RPRS.ml.m" ] }, { "page": "fun.RPRS.mm", "title": "Fit RS generalised lambda distribution to data set using moment matching", "topics": [ "fun.RPRS.mm" ] }, { "page": "fun.RPRS.qs", "title": "Fit RS generalised lambda distribution to data set using quantile matching", "topics": [ "fun.RPRS.qs" ] }, { "page": "fun.simu.bimodal", "title": "Simulate a mixture of two generalised lambda distributions.", "topics": [ "fun.simu.bimodal" ] }, { "page": "fun.theo.bi.mv.gld", "title": "Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.", "topics": [ "fun.theo.bi.mv.gld" ] }, { "page": "fun.theo.mv.gld", "title": "Find the theoretical first four moments of the generalised lambda distribution.", "topics": [ "fun.theo.mv.gld" ] }, { "page": "fun.which.zero", "title": "Determine which values are zero.", "topics": [ "fun.which.zero" ] }, { "page": "fun.zero.omit", "title": "Returns a vector after removing all the zeros.", "topics": [ "fun.zero.omit" ] }, { "page": "gl.check.lambda.alt", "title": "Checks whether the parameters provided constitute a valid generalised lambda distribution.", "topics": [ "gl.check.lambda.alt" ] }, { "page": "gl.check.lambda.alt1", "title": "Checks whether the parameters provided constitute a valid generalised lambda distribution.", "topics": [ "gl.check.lambda.alt1" ] }, { "page": "GLDfunctions", "title": "The Generalised Lambda Distribution Family", "topics": [ "dgl", "pgl", "qdgl", "qgl", "rgl" ] }, { "page": "histsu", "title": "Histogram with exact number of bins specified by the user", "topics": [ "histsu" ] }, { "page": "is.inf", "title": "Returns a logical vecto, TRUE if the value is Inf or -Inf.", "topics": [ "is.inf" ] }, { "page": "is.notinf", "title": "Returns a logical vector TRUE, if the value is not Inf or -Inf.", "topics": [ "is.notinf" ] }, { "page": "ks.gof", "title": "Kolmogorov-Smirnov test", "topics": [ "ks.gof" ] }, { "page": "Lmoments", "title": "L-moments", "concept": [ "moments", "skewness", "kurtosis", "descriptive statistics" ], "topics": [ "Lcoefs", "Lmomcov", "Lmomcov_calc", "Lmoments", "Lmoments_calc" ] }, { "page": "pretty.su", "title": "An alternative to the normal pretty function in R.", "topics": [ "pretty.su" ] }, { "page": "qqplot.gld", "title": "Do a quantile plot on the univariate distribution fits.", "topics": [ "qqplot.gld" ] }, { "page": "qqplot.gld.bi", "title": "Do a quantile plot on the bimodal distribution fits.", "topics": [ "qqplot.gld.bi" ] }, { "page": "QUnif", "title": "Quasi Randum Numbers via Halton Sequences", "concept": [ "Quasi Monte Carlo", "low 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