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#############################################################################
# Copyright (c) 2014 Mathieu Ribatet
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the
# Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA
#
#############################################################################
## In this function, several methods to estimates the GPD parameters
## are available:
## 1) Moments Estimator
## 2) Unbiased Probability Weighted Moment (PWMU) Estimator
## 3) Biased Probability Weighted Moment (PWMB) Estimator
## 4) Maximum Likelihood Estimator
## 5) Pickands' Estimator
## 6) Minimum Density Power Divergence Estimator
## 7) Method of Medians Estimator
## 8) Likelihood Moment Estimator
## 9) Maximum Goodness-of-Fit Estimator
## 10) Maximum penalized likelihood estimator
## A generic function for estimate the GPD parameters
fitgpd <- function(data, threshold, est = "mle", ...){
threshold.call <- deparse(threshold)
if (!(est %in% c("moments", "pwmb", "pwmu", "mle", "pickands",
"mdpd", "med", "lme", "mgf", "mple")))
stop("Unknown estimator. Please check the ``est'' argument.")
fitted <- switch(est, 'moments' = gpdmoments(data, threshold),
'pwmb' = gpdpwmb(data, threshold, ...),
'pwmu' = gpdpwmu(data, threshold),
'mle' = gpdmle(data, threshold, ...),
'pickands' = gpdpickands(data, threshold),
'mdpd' = gpdmdpd(data, threshold, ...),
'med' = gpdmed(data, threshold, ...),
'lme' = gpdlme(data, threshold, ...),
'mgf' = gpdmgf(data, threshold, ...),
'mple' = gpdmple(data, threshold, ...)
)
fitted$threshold.call <- threshold.call
class(fitted) <- c("uvpot","pot")
return(fitted)
}
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