Package: GLDEX 2.0.0.9.3

GLDEX: Fitting Single and Mixture of Generalised Lambda Distributions

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" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Authors:Steve Su [aut, cre, cph], Martin Maechler [aut], Juha Karvanen [aut], Robert King [aut], Benjamin Dean [ctb], R Core Team [aut]

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# Install 'GLDEX' in R:
install.packages('GLDEX', repos = c('https://allegropiano.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.22 score 3 packages 92 scripts 587 downloads 2 mentions 139 exports 3 dependencies

Last updated 1 years agofrom:57819c0530. Checks:OK: 7 NOTE: 2. Indexed: yes.

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Exports:C_check_gldC_dglC_mult_check_gldC_optim_fun3C_optim_fun3_vC_pglC_q_fmkl_gld_minmax_checkC_q_rs_gld_minmax_checkdgldigitsBasefun.auto.bimodal.mlfun.auto.bimodal.pmlfun.auto.bimodal.qsfun.auto.mm.fmklfun.auto.perc.rsfun.betafun.beta1fun.bimodal.fit.mlfun.bimodal.fit.pmlfun.bimodal.initfun.check.gldfun.check.gld.multifun.class.regime.bifun.comp.moments.mlfun.comp.moments.ml.2fun.data.fit.hsfun.data.fit.hs.nwfun.data.fit.lmfun.data.fit.mlfun.data.fit.mmfun.data.fit.qsfun.diag.ks.gfun.diag.ks.g.bimodalfun.diag1fun.diag2fun.disc.estimationfun.fit.gl.v.lmfun.fit.gl.v.lmafun.fit.gl.v2afun.fit.gl.v2a.nwfun.fit.gl.v2bfun.fit.gl.v2b.nwfun.fit.gl.v3fun.fit.gl.v3afun.fit.gl.v3mfun.fit.gl.v4fun.fit.gl.v4afun.fit.gl.v6fun.fit.gl.v6afun.fmklfun.fmkl.L30fun.fmkl.L40fun.fmkl.mm.minfun.fmkl.mm.solfun.fmkl.mm.sol.altfun.fmkl.nrfun.fmkl0fun.fmklafun.fmklbfun.gen.qrnfun.lambda.percentilefun.Lm.gt.2.fmklfun.Lm.gt.2.rsfun.lm.theo.gldfun.mApplyfun.minmax.check.gldfun.momentsfun.moments.bimodalfun.moments.rfun.nclass.efun.percentilefun.plot.fitfun.plot.fit.bmfun.plot.many.gldfun.rawmomentsfun.RMFMKL.hsfun.RMFMKL.hs.nwfun.RMFMKL.lmfun.RMFMKL.mlfun.RMFMKL.ml.mfun.RMFMKL.mmfun.RMFMKL.qsfun.RPRS.hsfun.RPRS.hs.nwfun.RPRS.lmfun.RPRS.mlfun.RPRS.ml.mfun.RPRS.mmfun.RPRS.qsfun.rs.nrfun.rs.perc.gradientfun.rs.perc.minfun.rs.perc.solfun.rs.perc.sol.altfun.rsbfun.simu.bimodalfun.theo.bi.mv.gldfun.theo.mv.gldfun.which.zerofun.zero.omitgl.check.lambda.altgl.check.lambda.alt1histsuis.infis.notinfks.gofkurtosisLcoefsLmomcovLmomcov_calcLmomentsLmoments_calcoptim.fun.bi.finaloptim.fun.lmoptim.fun.qsoptim.fun2optim.fun2.nwoptim.fun3optim.fun3.Coptim.fun3.C.moptim.fun4optim.fun5optim.fun6optim.fun7pglpretty.suqdglqglqqplot.gldqqplot.gld.biQUnifrglsHaltonskewnessstarshipstarship.adaptivegridstarship.objt1lmomentswhich.na

Dependencies:clusterRcppspacefillr

Readme and manuals

Help Manual

Help pageTopics
This package fits RS and FMKL generalised lambda distributions using various methods. It also provides functions for fitting bimodal distributions using mixtures of generalised lambda distributions.GLDEX-package GLDEX
Digit/Bit Representation of Integers in any BasedigitsBase
Fitting mixture of generalied lambda distribtions to data using maximum likelihood estimation via the EM algorithmfun.auto.bimodal.ml
Fitting mixture of generalied lambda distribtions to data using parition maximum likelihood estimationfun.auto.bimodal.pml
Fitting mixtures of generalied lambda distribtions to data using quantile matching methodfun.auto.bimodal.qs
Finds the final fits using the maximum likelihood estimation for the bimodal dataset.fun.bimodal.fit.ml
Finds the final fits using partition maximum likelihood estimation for the bimodal dataset.fun.bimodal.fit.pml
Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.fun.bimodal.init
Check whether the RS or FMKL/FKML GLD is a valid GLD for single values of L1, L2, L3 and L4fun.check.gld
Check whether the RS or FMKL/FKML GLD is a valid GLD for vectors of L1, L2, L3 and L4fun.check.gld.multi
Classifies data into two groups using a clustering regime.fun.class.regime.bi
Compare the moments of the data and the fitted univariate generalised lambda distribution.fun.comp.moments.ml
Compare the moments of the data and the fitted univariate generalised lambda distribution. Specialised funtion designed for RMFMKL.ML and STAR methods.fun.comp.moments.ml.2
Fit RS and FMKL generalised distributions to data using discretised approach with weights.fun.data.fit.hs
Fit RS and FMKL generalised distributions to data using discretised approach without weights.fun.data.fit.hs.nw
Fit data using L moment matching estimation for RS and FMKL GLDfun.data.fit.lm
Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.fun.data.fit.ml
Fit data using moment matching estimation for RS and FMKL GLDfun.data.fit.mm
Fit data using quantile matching estimation for RS and FMKL GLDfun.data.fit.qs
Compute the simulated Kolmogorov-Smirnov tests for the unimodal datasetfun.diag.ks.g
Compute the simulated Kolmogorov-Smirnov tests for the bimodal datasetfun.diag.ks.g.bimodal
Diagnostic function for theoretical distribution fits through the resample Kolmogorov-Smirnoff testsfun.diag1
Diagnostic function for empirical data distribution fits through the resample Kolmogorov-Smirnoff testsfun.diag2
Estimates the mean and variance after cutting up a vector of variable into evenly spaced categories.fun.disc.estimation
Finds the low discrepancy quasi random numbersfun.gen.qrn
Find the theoretical first four L moments of the generalised lambda distribution.fun.lm.theo.gld
Applying functions based on an index for a matrix.fun.mApply
Check whether the specified GLDs cover the minimum and the maximum values in a datasetfun.minmax.check.gld
Finds the moments of fitted mixture of generalised lambda distribution by simulation.fun.moments.bimodal
Calculate mean, variance, skewness and kurtosis of a numerical vectorfun.moments.r
Estimates the number of classes or bins to smooth over in the discretised method of fitting generalised lambda distribution to data.fun.nclass.e
Plotting the univariate generalised lambda distribution fits on the data set.fun.plot.fit
Plotting mixture of two generalised lambda distributions on the data set.fun.plot.fit.bm
Plotting many univariate generalised lambda distributions on one page.fun.plot.many.gld
Computes the raw moments of the generalised lambda distribution up to 4th order.fun.rawmoments
Fit FMKL generalised distribution to data using discretised approach with weights.fun.RMFMKL.hs
Fit FMKL generalised distribution to data using discretised approach without weights.fun.RMFMKL.hs.nw
Fit FMKL generalised lambda distribution to data set using L moment matchingfun.RMFMKL.lm
Fit FMKL generalised lambda distribution to data set using maximum likelihood estimationfun.RMFMKL.ml
Fit RS generalised lambda distribution to data set using maximum likelihood estimationfun.RMFMKL.ml.m
Fit FMKL generalised lambda distribution to data set using moment matchingfun.RMFMKL.mm
Fit FMKL generalised lambda distribution to data set using quantile matchingfun.RMFMKL.qs
Fit RS generalised distribution to data using discretised approach with weights.fun.RPRS.hs
Fit RS generalised distribution to data using discretised approach without weights.fun.RPRS.hs.nw
Fit RS generalised lambda distribution to data set using L moment matchingfun.RPRS.lm
Fit RS generalised lambda distribution to data set using maximum likelihood estimationfun.RPRS.ml
Fit RS generalised lambda distribution to data set using maximum likelihood estimationfun.RPRS.ml.m
Fit RS generalised lambda distribution to data set using moment matchingfun.RPRS.mm
Fit RS generalised lambda distribution to data set using quantile matchingfun.RPRS.qs
Simulate a mixture of two generalised lambda distributions.fun.simu.bimodal
Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.fun.theo.bi.mv.gld
Find the theoretical first four moments of the generalised lambda distribution.fun.theo.mv.gld
Determine which values are zero.fun.which.zero
Returns a vector after removing all the zeros.fun.zero.omit
Checks whether the parameters provided constitute a valid generalised lambda distribution.gl.check.lambda.alt
Checks whether the parameters provided constitute a valid generalised lambda distribution.gl.check.lambda.alt1
The Generalised Lambda Distribution Familydgl pgl qdgl qgl rgl
Histogram with exact number of bins specified by the userhistsu
Returns a logical vecto, TRUE if the value is Inf or -Inf.is.inf
Returns a logical vector TRUE, if the value is not Inf or -Inf.is.notinf
Kolmogorov-Smirnov testks.gof
L-momentsLcoefs Lmomcov Lmomcov_calc Lmoments Lmoments_calc
An alternative to the normal pretty function in R.pretty.su
Do a quantile plot on the univariate distribution fits.qqplot.gld
Do a quantile plot on the bimodal distribution fits.qqplot.gld.bi
Quasi Randum Numbers via Halton SequencesQUnif sHalton
Compute skewness and kurtosis statisticskurtosis skewness
Carry out the ``starship'' estimation method for the generalised lambda distributionstarship
Carry out the ``starship'' estimation method for the generalised lambda distribution using a grid-based searchstarship.adaptivegrid
Objective function that is minimised in starship estimation methodstarship.obj
Trimmed L-momentst1lmoments
Determine Missing Valueswhich.na