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Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package.
Version: | 0.5.0 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp, coda, parallel, methods, RcppProgress, graphics, stats, tools |
LinkingTo: | Rcpp, RcppEigen, RcppProgress |
Suggests: | bayesplot, knitr, rmarkdown, webshot, ggplot2, MASS |
Published: | 2024-02-01 |
DOI: | 10.32614/CRAN.package.bayesPO |
Author: | Guido Alberti Moreira [cre, aut] |
Maintainer: | Guido Alberti Moreira <guidoalber at gmail.com> |
Contact: | Guido Alberti Moreira <guidoalber@gmail.com> |
License: | GPL-3 |
NeedsCompilation: | yes |
CRAN checks: | bayesPO results |
Reference manual: | bayesPO.pdf |
Vignettes: |
bayesPO |
Package source: | bayesPO_0.5.0.tar.gz |
Windows binaries: | r-devel: bayesPO_0.5.0.zip, r-release: bayesPO_0.5.0.zip, r-oldrel: bayesPO_0.5.0.zip |
macOS binaries: | r-release (arm64): bayesPO_0.5.0.tgz, r-oldrel (arm64): bayesPO_0.5.0.tgz, r-release (x86_64): bayesPO_0.5.0.tgz, r-oldrel (x86_64): bayesPO_0.5.0.tgz |
Old sources: | bayesPO 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.