| 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 |
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