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Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log–concave density function. This procedure is justified by the fact that the GPD density is log–concave for gamma in [-1,0].
Version: | 2.0.5 |
Depends: | logcondens (≥ 2.0.0) |
Imports: | stats |
Published: | 2016-07-13 |
DOI: | 10.32614/CRAN.package.smoothtail |
Author: | Kaspar Ru{f}{i}bach and Samuel Mueller |
Maintainer: | Kaspar Rufibach <kaspar.rufibach at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | http://www.kasparrufibach.ch, www.maths.usyd.edu.au/ut/people?who=S_Mueller |
NeedsCompilation: | no |
Materials: | NEWS |
CRAN checks: | smoothtail results |
Reference manual: | smoothtail.pdf |
Package source: | smoothtail_2.0.5.tar.gz |
Windows binaries: | r-devel: smoothtail_2.0.5.zip, r-release: smoothtail_2.0.5.zip, r-oldrel: smoothtail_2.0.5.zip |
macOS binaries: | r-release (arm64): smoothtail_2.0.5.tgz, r-oldrel (arm64): smoothtail_2.0.5.tgz, r-release (x86_64): smoothtail_2.0.5.tgz, r-oldrel (x86_64): smoothtail_2.0.5.tgz |
Old sources: | smoothtail archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.