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GLDEX
View on CRAN: Click
here
Download and install GLDEX package within the R console
Install from CRAN:
install.packages("GLDEX")
Install from Github:
library("remotes")
install_github("cran/GLDEX") Install by package version:
library("remotes")
install_version("GLDEX", "2.0.0.9.4") Attach the package and use:
library("GLDEX")
Maintained by
Steve Su
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-12
Latest Update: 2025-07-23
Description:
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" , King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" , Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" , Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" , Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" , Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" , Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" , Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" , Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model".
How to cite:
Steve Su (2022). GLDEX: Fitting Single and Mixture of Generalised Lambda Distributions. R package version 2.0.0.9.4, https://cran.r-project.org/web/packages/GLDEX. Accessed 09 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for GLDEX
Functions, R codes and Examples using
the GLDEX R package
Some associated functions: FMKLfittingandbasicfunctions . GLDEX.package . GLDfunctions . Lmoments . QUnif . RSfittingandbasicfunctions . digitsBase . fittingfunctions . fun.RMFMKL.hs.nw . fun.RMFMKL.hs . fun.RMFMKL.lm . fun.RMFMKL.ml.m . fun.RMFMKL.ml . fun.RMFMKL.mm . fun.RMFMKL.qs . fun.RPRS.hs.nw . fun.RPRS.hs . fun.RPRS.lm . fun.RPRS.ml.m . fun.RPRS.ml . fun.RPRS.mm . fun.RPRS.qs . fun.auto.bimodal.ml . fun.auto.bimodal.pml . fun.auto.bimodal.qs . fun.beta . fun.bimodal.fit.ml . fun.bimodal.fit.pml . fun.bimodal.init . fun.check.gld.multi . fun.check.gld . fun.class.regime.bi . fun.comp.moments.ml.2 . fun.comp.moments.ml . fun.data.fit.hs.nw . fun.data.fit.hs . fun.data.fit.lm . fun.data.fit.ml . fun.data.fit.mm . fun.data.fit.qs . fun.diag.ks.g.bimodal . fun.diag.ks.g . fun.diag1 . fun.diag2 . fun.disc.estimation . fun.gen.qrn . fun.lm.theo.gld . fun.mApply . fun.minmax.check.gld . fun.moments.bimodal . fun.moments.r . fun.nclass.e . fun.plot.fit.bm . fun.plot.fit . fun.plot.many.gld . fun.rawmoments . fun.simu.bimodal . fun.theo.bi.mv.gld . fun.theo.mv.gld . fun.which.zero . fun.zero.omit . gl.check.lambda.alt . gl.check.lambda.alt1 . hiddenfunctions . histsu . is.inf . is.notinf . ks.gof . optimisationfunctions . pretty.su . qqplot.gld.bi . qqplot.gld . skewnessandkurtosis . starship.adaptivegrid . starship . starshipj . t1lmoments . which.na .
Some associated R codes: Lmoments.R . Lmoments_calc.R . QUnif.R . dgl.R . digitsBase.R . fun.Lm.gt.2.fmkl.R . fun.Lm.gt.2.rs.R . fun.RMFMKL.hs.R . fun.RMFMKL.hs.nw.R . fun.RMFMKL.lm.R . fun.RMFMKL.ml.R . fun.RMFMKL.ml.m.R . fun.RMFMKL.mm.R . fun.RMFMKL.qs.R . fun.RPRS.hs.R . fun.RPRS.hs.nw.R . fun.RPRS.lm.R . fun.RPRS.ml.R . fun.RPRS.ml.m.R . fun.RPRS.mm.R . fun.RPRS.qs.R . fun.auto.bimodal.ml.R . fun.auto.bimodal.pml.R . fun.auto.bimodal.qs.R . fun.auto.mm.fmkl.R . fun.auto.perc.rs.R . fun.beta.R . fun.beta1.R . fun.bimodal.fit.ml.R . fun.bimodal.fit.pml.R . fun.bimodal.init.R . fun.check.gld.R . fun.check.gld.multi.R . fun.class.regime.bi.R . fun.comp.moments.ml.2.R . fun.comp.moments.ml.R . fun.data.fit.hs.R . fun.data.fit.hs.nw.R . fun.data.fit.lm.R . fun.data.fit.ml.R . fun.data.fit.mm.R . fun.data.fit.qs.R . fun.diag.ks.g.R . fun.diag.ks.g.bimodal.R . fun.diag1.R . fun.diag2.R . fun.disc.estimation.R . fun.fit.gl.v.lm.R . fun.fit.gl.v.lma.R . fun.fit.gl.v2a.R . fun.fit.gl.v2a.nw.R . fun.fit.gl.v2b.R . fun.fit.gl.v2b.nw.R . fun.fit.gl.v3.R . fun.fit.gl.v3a.R . fun.fit.gl.v3m.R . fun.fit.gl.v4.R . fun.fit.gl.v4a.R . fun.fit.gl.v6.R . fun.fit.gl.v6a.R . fun.fmkl.L30.R . fun.fmkl.L40.R . fun.fmkl.R . fun.fmkl.mm.min.R . fun.fmkl.mm.sol.R . fun.fmkl.mm.sol.alt.R . fun.fmkl.nr.R . fun.fmkl0.R . fun.fmkla.R . fun.fmklb.R . fun.gen.qrn.R . fun.lambda.percentile.R . fun.lm.theo.gld.R . fun.mApply.R . fun.minmax.check.gld.R . fun.moments.R . fun.moments.bimodal.R . fun.moments.r.R . fun.nclass.e.R . fun.percentile.R . fun.plot.fit.R . fun.plot.fit.bm.R . fun.plot.many.gld.R . fun.rawmoments.R . fun.rs.nr.R . fun.rs.perc.gradient.R . fun.rs.perc.min.R . fun.rs.perc.sol.R . fun.rs.perc.sol.alt.R . fun.rsb.R . fun.simu.bimodal.R . fun.theo.bi.mv.gld.R . fun.theo.mv.gld.R . fun.which.zero.R . fun.zero.omit.R . gl.check.lambda.alt.R . gl.check.lambda.alt1.R . hiddenfunctions.R . histsu.R . is.inf.R . is.notinf.R . ks.gof.R . kurtosis.R . optim.fun.bi.final.R . optim.fun.lm.R . optim.fun.qs.R . optim.fun2.R . optim.fun2.nw.R . optim.fun3.C.R . optim.fun3.C.m.R . optim.fun3.R . optim.fun4.R . optim.fun5.R . optim.fun6.R . optim.fun7.R . pgl.R . pretty.su.R . qdgl.R . qgl.R . qqplot.gld.R . qqplot.gld.bi.R . rgl.R . sHalton.R . skewness.R . starship.R . t1lmoments.R . which.na.R . Full GLDEX package functions and examples
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