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extrememix: Bayesian Estimation of Extreme Value Mixture Models

Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.

Version: 0.0.1
Depends: R (≥ 2.10)
Imports: evd, ggplot2, gridExtra, mixtools, Rcpp, RcppProgress, stats, threshr
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown
Published: 2024-10-04
DOI: 10.32614/CRAN.package.extrememix
Author: Manuele Leonelli ORCID iD [aut, cre, cph]
Maintainer: Manuele Leonelli <manuele.leonelli at ie.edu>
BugReports: https://github.com/manueleleonelli/extrememix/issues
License: MIT + file LICENSE
URL: https://github.com/manueleleonelli/extrememix
NeedsCompilation: yes
Citation: extrememix citation info
Materials: README NEWS
CRAN checks: extrememix results

Documentation:

Reference manual: extrememix.pdf
Vignettes: overview (source, R code)

Downloads:

Package source: extrememix_0.0.1.tar.gz
Windows binaries: r-devel: not available, r-release: extrememix_0.0.1.zip, r-oldrel: extrememix_0.0.1.zip
macOS binaries: r-release (arm64): extrememix_0.0.1.tgz, r-oldrel (arm64): extrememix_0.0.1.tgz, r-release (x86_64): extrememix_0.0.1.tgz, r-oldrel (x86_64): extrememix_0.0.1.tgz

Linking:

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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.