README

The analysis of univariate distributions is a complex subject. This
is particularly the case with heavy-tailed data.  The L-moments, 
which have similar meaning as the ordinary (product or central) moments, 
have revolutionized many fields including statistical hydrology in which 
your author operates. L-moments have many properties that make them 
extremely attractive. These properties include unbiasedness, efficiency, 
consistency, robustness, and others. The R package contained here fully 
implements L-moments in the context of many probability distributions 
including the Exponential, Gamma, Gumbel, Normal, Generalized Extreme Value, 
Generalized Logistic, Generalized Normal (log-Normal), Generalized Pareto, 
Pearson Type III, Kappa, Wakeby, and Weibull. This package provides core 
functions and numerous ancillary functions to help get the user started and 
to keep the user entertained by building complex analysis applications. Very 
recent and extremely exciting developments have extended L-moments to multi-
variate analysis--the sample L-comoments are implemented here on an experimental 
basis. The package also implements the trimmed L-moments and support for the 
Cauchy, Generalized Lambda, and Generalized Pareto distributions.

Things to do:
(1) Continue to explore parameter estimation of Generalized Lambda distribution.
(2) Add additional distributions (uniform, others).
(3) Make further progress on documentation including nontrivial examples.
(4) Further "design" the L-moment object for greater function portability.
      Deprecate the object design from lmom.ub and similar.
(5) Continue discussions with Juha Karvanen on a cross-package L-moment 
      object. (Related to ToDo no. 4.)
            
wha (April 30, 2006)

See inst/doc/WARRANTY for lack-of-warranty information.

See the excellent R-package Lmoments by Juha Karvanen.

Note: This is a large package with hundreds of examples. The examples will 
take considerable time to process on 'R CMD check lmomco'.

