The package implements the statistical theory of L-moments including
L-moment estimation, probability-weighted moment estimation, parameter
estimation for numerous familiar and not-so-familiar distributions, and
L-moment estimation for the same distributions from the parameters.
L-moments are derived from the expectations of order statistics and are
linear with respect to the probability- weighted moments. L-moments are
directly analogous to the well-known product moments; however, L-moments
have many advantages including unbiasedness, robustness, and consistency
with respect to the product moments. This package is oriented around the
FORTRAN algorithms of J.R.M. Hosking, and the nomenclature for many of the
functions parallels that of the Hosking library. However, numerous
extensions are made to aid in expand of the breadth and ease of L-moment
application. Much theoretical extension of L-moment theory has occurred in
recent years. E.A.H. Elamir and A.H. Seheult have developed the trimmed
L-moments, which are implemented in this package. Further, recent
developments by Robert Serfling and Peng Xiao have extended L-moments into
multivariate space; the so-called sample L-comoments are implemented here.
The supported distributions with moment type shown as L (L-moments) or TL
(trimmed L-moments) and
additional