GLDEX

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.