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.
Lmoments                L-moments
QUnif                   Quasi Randum Numbers via Halton Sequences
dgl                     The Generalised Lambda Distribution Family
digitsBase              Digit/Bit Representation of Integers in any
                        Base
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.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.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.
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
pretty.su               An alternative to the normal pretty function in
                        R.
qqplot.gld              Do a quantile plot on the univariate
                        distribution fits.
qqplot.gld.bi           Do a quantile plot on the bimodal distribution
                        fits.
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
