Functions related to L-moments: computation of L-moments
and trimmed L-moments of distributions and data samples;
parameter estimation; L-moment ratio diagram; plot vs.
quantiles of an extreme-value distribution.

Functions related to L-moments: computation of L-moments
and trimmed L-moments of distributions and data samples; parameter
estimation; L-moment ratio diagram; plot vs. quantiles of an
extreme-value distribution.

(Precipitation) Frequency Analysis and Variability with
L-Moments from 'lmom'
Emanuele Cordano, Unit D.02 Water and Marine Resources,Joint Research
Centre of the European Commission
It is an extension of 'lmom' R package: 'pel','cdf',qua' function
families are lumped and called from one function per each family respectively in order to
create robust automatic tools to fit data  with different probability
distributions and then to estimate probability values and return periods.  The implemented functions are able to manage time series with constant and/or missing values without stopping
the execution with error messages. The package also contains tools to  calculate several  indices based on variability (e.g. 'SPI' , Standardized
Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatio-temporal gridded values.

Regional frequency analysis using L-moments
Functions for regional frequency analysis using the methods
of J.R.M. Hosking and J.R. Wallis (1997), "Regional frequency analysis:
an approach based on L-moments".

Functions for regional frequency analysis using the
methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional
frequency analysis: an approach based on L-moments".

Functions for regional frequency analysis using the methods
of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
an approach based on L-moments".

Regional Frequency Analysis using L-Moments

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.
Further features are added to aid in extension of the breadth of L-moment
application. Additionally, recent developments by Robert Serfling and Peng Xiao
have extended L-moments into multivariate space; the so-called sample L-comoments
are implemented here.