Description: Peaks Over Threshold (POT) or 'methode du renouvellement'. The
distribution for the excesses can be chosen, and heterogeneous data
(including historical data or block data) can be used in a

Author: Yves Deville <deville.yves@alpestat.com> and IRSN
Description: Graphical User Interface for Renext.
Depends: gWidgets, gWidgetstcltk(>= 0.0-44), Renext(>= 1.5-0)

Author: Yves Deville <deville.yves@alpestat.com> and IRSN <renext@irsn.fr>
Description: Graphical User Interface for Renext

Depends: graphics, stats, utils, gWidgets, gWidgetstcltk(>= 0.0-44),

Title: Interface to Allow Full Use of the Environment Modules System
Description: Provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.

Description: Provides a general routine, envMU(), which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU() does not presume a model.  This package implements response envelopes (env()),  partial response envelopes (penv()),  envelopes in the predictor space (xenv()),  heteroscedastic envelopes (henv()),  simultaneous envelopes (stenv()),  scaled response envelopes (senv()),  scaled envelopes in the predictor space (sxenv()),  groupwise envelopes (genv()), weighted envelopes (weighted.env(), weighted.penv() and weighted.xenv()),  envelopes in logistic regression (logit.env()), and envelopes in Poisson regression (pois.env()). For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>.  Optimization is based on a clockwise coordinate descent algorithm.