R: Fitting Single and Mixture of Generalised Lambda Distributions

# Fitting Single and Mixture of Generalised Lambda Distributions

## Documentation for package ‘GLDEX’ version 2.0.0.9.4

- DESCRIPTION file

## Help Pages

GLDEX-package | 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.
dgl | The Generalised Lambda Distribution Family
digitsBase | Digit/Bit Representation of Integers in any Base
fun.auto.bimodal.ml | Fitting mixture of generalied lambda distribtions to data using maximum likelihood estimation via the EM algorithm
fun.auto.bimodal.pml | Fitting mixture of generalied lambda distribtions to data using parition maximum likelihood estimation
fun.auto.bimodal.qs | Fitting mixtures of generalied lambda distribtions to data using quantile matching method
fun.bimodal.fit.ml | Finds the final fits using the maximum likelihood estimation for the bimodal dataset.
fun.bimodal.fit.pml | Finds the final fits using partition maximum likelihood estimation for the bimodal dataset.
fun.bimodal.init | Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.
fun.check.gld | Check whether the RS or FMKL/FKML GLD is a valid GLD for single values of L1, L2, L3 and L4
fun.check.gld.multi | Check whether the RS or FMKL/FKML GLD is a valid GLD for vectors of L1, L2, L3 and L4
fun.class.regime.bi | Classifies data into two groups using a clustering regime.
fun.comp.moments.ml | Compare the moments of the data and the fitted univariate generalised lambda distribution.
fun.comp.moments.ml.2 | Compare the moments of the data and the fitted univariate generalised lambda distribution. Specialised funtion designed for RMFMKL.ML and STAR methods.
fun.data.fit.hs | Fit RS and FMKL generalised distributions to data using discretised approach with weights.
fun.data.fit.hs.nw | Fit RS and FMKL generalised distributions to data using discretised approach without weights.
fun.data.fit.lm | Fit data using L moment matching estimation for RS and FMKL GLD
fun.data.fit.ml | Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.
fun.data.fit.mm | Fit data using moment matching estimation for RS and FMKL GLD
fun.data.fit.qs | Fit data using quantile matching estimation for RS and FMKL GLD
fun.diag.ks.g | Compute the simulated Kolmogorov-Smirnov tests for the unimodal dataset
fun.diag.ks.g.bimodal | Compute the simulated Kolmogorov-Smirnov tests for the bimodal dataset
fun.diag1 | Diagnostic function for theoretical distribution fits through the resample Kolmogorov-Smirnoff tests
fun.diag2 | Diagnostic function for empirical data distribution fits through the resample Kolmogorov-Smirnoff tests
fun.disc.estimation | Estimates the mean and variance after cutting up a vector of variable into evenly spaced categories.
fun.gen.qrn | Finds the low discrepancy quasi random numbers
fun.lm.theo.gld | Find the theoretical first four L moments of the generalised lambda distribution.
fun.mApply | Applying functions based on an index for a matrix.
fun.minmax.check.gld | Check whether the specified GLDs cover the minimum and the maximum values in a dataset
fun.moments.bimodal | Finds the moments of fitted mixture of generalised lambda distribution by simulation.
fun.moments.r | Calculate mean, variance, skewness and kurtosis of a numerical vector
fun.nclass.e | Estimates the number of classes or bins to smooth over in the discretised method of fitting generalised lambda distribution to data.
fun.plot.fit | Plotting the univariate generalised lambda distribution fits on the data set.
fun.plot.fit.bm | Plotting mixture of two generalised lambda distributions on the data set.
fun.plot.many.gld | Plotting many univariate generalised lambda distributions on one page.
fun.rawmoments | Computes the raw moments of the generalised lambda distribution up to 4th order.
fun.RMFMKL.hs | Fit FMKL generalised distribution to data using discretised approach with weights.
fun.RMFMKL.hs.nw | Fit FMKL generalised distribution to data using discretised approach without weights.
fun.RMFMKL.lm | Fit FMKL generalised lambda distribution to data set using L moment matching
fun.RMFMKL.ml | Fit FMKL generalised lambda distribution to data set using maximum likelihood estimation
fun.RMFMKL.ml.m | Fit RS generalised lambda distribution to data set using maximum likelihood estimation
fun.RMFMKL.mm | Fit FMKL generalised lambda distribution to data set using moment matching
fun.RMFMKL.qs | Fit FMKL generalised lambda distribution to data set using quantile matching
fun.RPRS.hs | Fit RS generalised distribution to data using discretised approach with weights.
fun.RPRS.hs.nw | Fit RS generalised distribution to data using discretised approach without weights.
fun.RPRS.lm | Fit RS generalised lambda distribution to data set using L moment matching
fun.RPRS.ml | Fit RS generalised lambda distribution to data set using maximum likelihood estimation
fun.RPRS.ml.m | Fit RS generalised lambda distribution to data set using maximum likelihood estimation
fun.RPRS.mm | Fit RS generalised lambda distribution to data set using moment matching
fun.RPRS.qs | Fit RS generalised lambda distribution to data set using quantile matching
fun.simu.bimodal | Simulate a mixture of two generalised lambda distributions.
fun.theo.bi.mv.gld | Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.
fun.theo.mv.gld | Find the theoretical first four moments of the generalised lambda distribution.
fun.which.zero | Determine which values are zero.
fun.zero.omit | Returns a vector after removing all the zeros.
gl.check.lambda.alt | Checks whether the parameters provided constitute a valid generalised lambda distribution.
gl.check.lambda.alt1 | Checks whether the parameters provided constitute a valid generalised lambda distribution.
GLDEX | 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.
histsu | Histogram with exact number of bins specified by the user
is.inf | Returns a logical vecto, TRUE if the value is Inf or -Inf.
is.notinf | Returns a logical vector TRUE, if the value is not Inf or -Inf.
ks.gof | Kolmogorov-Smirnov test
kurtosis | Compute skewness and kurtosis statistics
Lcoefs | L-moments
Lmomcov | L-moments
Lmomcov_calc | L-moments
Lmoments | L-moments
Lmoments_calc | L-moments
pgl | The Generalised Lambda Distribution Family
pretty.su | An alternative to the normal pretty function in R.
qdgl | The Generalised Lambda Distribution Family
qgl | The Generalised Lambda Distribution Family
qqplot.gld | Do a quantile plot on the univariate distribution fits.
qqplot.gld.bi | Do a quantile plot on the bimodal distribution fits.
QUnif | Quasi Randum Numbers via Halton Sequences
rgl | The Generalised Lambda Distribution Family
sHalton | Quasi Randum Numbers via Halton Sequences
skewness | Compute skewness and kurtosis statistics
starship | Carry out the "starship" estimation method for the generalised lambda distribution
starship.adaptivegrid | Carry out the "starship" estimation method for the generalised lambda distribution using a grid-based search
starship.obj | Objective function that is minimised in starship estimation method
t1lmoments | Trimmed L-moments
which.na | Determine Missing Values