Uses Stuart Coles' (Coles, Stewart, "An introduction to
statistical modeling of extreme values", Springer-Verlag, London 2001)
S-plus functions as ported to the R programming language (ismev) by Alec
Stephenson (http://www.maths.lancs.ac.uk/~stephena/).  This toolkit provides
GUI windows for pedagogical purposes.

Uses Stuart Coles' (Coles, Stewart, "An introduction to
statistical modeling of extreme values", Springer-Verlag, London 2001)
S-plus functions as ported to the R programming language (ismev) by Alec
Stephenson (http://www.maths.lancs.ac.uk/~stephena/).  This toolkit provides
a Graphical User Interface (GUI) to ismev for pedagogical purposes.

Uses Stuart Coles' (Coles, Stewart, "An introduction to
statistical modeling of extreme values", Springer-Verlag,
London 2001) S-plus functions as ported to the R programming
language (ismev) by Alec Stephenson
(http://www.maths.lancs.ac.uk/~stephena/).  This toolkit
provides a Graphical User Interface (GUI) to ismev for
pedagogical purposes.

This toolkit provides a Graphical User Interface (GUI) to
various functions for doing extreme value analysis.

R (>= 2.10.0), Lmoments, distillery, car
Functions for performing extreme value analysis.

R (>= 2.10.0), Lmoments, distillery (>= 1.0-4), car

General functions for performing extreme value analysis.  In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes.  Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping.  Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included.  For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.

Estimate the lower extreme quantile with the censored Weibull
MLE and censored Weibull Mixture
This package implements the subjectively censored Weibull MLE and censored Weibull mixture
for the lower quantile estimation. It also includes functions to evaluation the standard error of the resulting
quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for
the Type-I or Type-II right censored data.