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.

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs),
  distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate
  Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and
  maximum product spacings estimation are available. Right-tail and left-tail LM censoring
  by threshold or indicator variable are available. LMs of residual (resid) and reversed
  (rev) residual life are implemented along with 13 quantile operators for reliability analyses.
  Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available.
  Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added
  (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L],
  Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp
  Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L],
  Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L],
  Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L],
  3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L],
  Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L],
  Truncated Exponential [L], Wakeby [L], and Weibull [L].

Contains functions to estimate
        L-moments and trimmed L-moments from the data. Also
        contains functions to estimate the parameters of the normal
        polynomial quantile mixture and the Cauchy polynomial quantile
        mixture from L-moments and trimmed L-moments.

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 spatially gridded values.