GLDEX

Low Discrepancy Sequences

This is a collection of functions designed to find the initial values under the method of moments for FMKL generalised lambda distribution. It also includes basic FMKL GLD functions.

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.

The Generalised Lambda Distribution Family

L-moments

Quasi Randum Numbers via Halton Sequences

This is a collection of functions designed to find the initial values of method of moments for RS generalised lambda distribution. It also includes basic RS GLD functions.

Digit/Bit Representation of Integers in any Base

This is a collection of functions designed to implement the fitting algorithms for all the methods covered in this package.

Fit FMKL generalised distribution to data using discretised approach with weights.

Fit FMKL generalised distribution to data using discretised approach without weights.

Fit FMKL generalised lambda distribution to data set using L moment matching

Fit FMKL generalised lambda distribution to data set using maximum likelihood estimation

Fit RS generalised lambda distribution to data set using maximum likelihood estimation

Fit FMKL generalised lambda distribution to data set using moment matching

Fit FMKL generalised lambda distribution to data set using quantile matching

Fit RS generalised distribution to data using discretised approach with weights.

Fit RS generalised distribution to data using discretised approach without weights.

Fit RS generalised lambda distribution to data set using L moment matching

Fit RS generalised lambda distribution to data set using maximum likelihood estimation

Fit RS generalised lambda distribution to data set using maximum likelihood estimation

Fit RS generalised lambda distribution to data set using moment matching

Fit RS generalised lambda distribution to data set using quantile matching

Fitting mixture of generalied lambda distribtions to data using maximum likelihood estimation via the EM algorithm

Fitting mixture of generalied lambda distribtions to data using parition maximum likelihood estimation

Fitting mixtures of generalied lambda distribtions to data using quantile matching method

This is a collection of functions used in the calculation of the beta function.

Finds the final fits using the maximum likelihood estimation for the bimodal dataset.

Finds the final fits using partition maximum likelihood estimation for the bimodal dataset.

Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.

Check whether the RS or FMKL/FKML GLD is a valid GLD for single values of L1, L2, L3 and L4

Check whether the RS or FMKL/FKML GLD is a valid GLD for vectors of L1, L2, L3 and L4

Classifies data into two groups using a clustering regime.

Compare the moments of the data and the fitted univariate generalised lambda distribution.

Compare the moments of the data and the fitted univariate generalised lambda distribution. Specialised funtion designed for RMFMKL.ML and STAR methods.

Fit RS and FMKL generalised distributions to data using discretised approach with weights.

Fit RS and FMKL generalised distributions to data using discretised approach without weights.

Fit data using L moment matching estimation for RS and FMKL GLD

Fit data using RS, FMKL maximum likelihood estimation and the FMKL starship method.

Fit data using moment matching estimation for RS and FMKL GLD

Fit data using quantile matching estimation for RS and FMKL GLD

Compute the simulated Kolmogorov-Smirnov tests for the unimodal dataset

Compute the simulated Kolmogorov-Smirnov tests for the bimodal dataset

Diagnostic function for theoretical distribution fits through the resample Kolmogorov-Smirnoff tests

Diagnostic function for empirical data distribution fits through the resample Kolmogorov-Smirnoff tests

Estimates the mean and variance after cutting up a vector of variable into evenly spaced categories.

Finds the low discrepancy quasi random numbers

Find the theoretical first four L moments of the generalised lambda distribution.

Applying functions based on an index for a matrix.

Check whether the specified GLDs cover the minimum and the maximum values in a dataset

Finds the moments of fitted mixture of generalised lambda distribution by simulation.

Calculate mean, variance, skewness and kurtosis of a numerical vector

Estimates the number of classes or bins to smooth over in the discretised method of fitting generalised lambda distribution to data.

Plotting the univariate generalised lambda distribution fits on the data set.

Plotting mixture of two generalised lambda distributions on the data set.

Plotting many univariate generalised lambda distributions on one page.

Computes the raw moments of the generalised lambda distribution up to 4th order.

Simulate a mixture of two generalised lambda distributions.

Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.

Find the theoretical first four moments of the generalised lambda distribution.

Determine which values are zero.

Returns a vector after removing all the zeros.

Checks whether the parameters provided constitute a valid generalised lambda distribution.

Checks whether the parameters provided constitute a valid generalised lambda distribution.

This is a collection of functions designed to implement the basic GLD functions.

Histogram with exact number of bins specified by the user

Returns a logical vecto, TRUE if the value is Inf or -Inf.

Returns a logical vector TRUE, if the value is not Inf or -Inf.

Kolmogorov-Smirnov test

This is a collection of functions used in the optimisation processes for all the fitting methods covered in this package.

An alternative to the normal pretty function in R.

Do a quantile plot on the univariate distribution fits.

Do a quantile plot on the bimodal distribution fits.

Compute skewness and kurtosis statistics

Carry out the “starship” estimation method for the generalised lambda distribution

Carry out the “starship” estimation method for the generalised lambda distribution using a grid-based search

Objective function that is minimised in starship estimation method

Trimmed L-moments

Determine Missing Values