Package: hSDM 1.4.4
Ghislain Vieilledent
hSDM: Hierarchical Bayesian Species Distribution Models
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Authors:
hSDM_1.4.4.tar.gz
hSDM_1.4.4.tar.gz(r-4.5-noble)hSDM_1.4.4.tar.gz(r-4.4-noble)
hSDM_1.4.4.tgz(r-4.4-emscripten)hSDM_1.4.4.tgz(r-4.3-emscripten)
hSDM.pdf |hSDM.html✨
hSDM/json (API)
NEWS
| # Install 'hSDM' in R: |
| install.packages('hSDM', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/ghislainv/hsdm/issues
Pkgdown site:https://ecology.ghislainv.fr
- altitude - Virtual altitudinal data
- cfr.env - Environmental data for South Africa's Cap Floristic Region
- data.Kery2010 - Count data for the Willow tit
- datacells.Latimer2006 - Data of presence-absence
- frogs - Counts of the number of frogs in a water body
- neighbors.Latimer2006 - Neighborhood data
- punc10 - Occurrence data for _Protea punctata_ Meisn. in the Cap Floristic Region
Last updated 2 years agofrom:23c17415d9. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 24 2025 |
| R-4.5-linux-x86_64 | OK | Mar 24 2025 |
| R-4.4-linux-x86_64 | OK | Mar 24 2025 |
Exports:hSDM.binomialhSDM.binomial.iCARhSDM.NmixturehSDM.Nmixture.iCARhSDM.Nmixture.KhSDM.poissonhSDM.poisson.iCARhSDM.siteocchSDM.siteocc.iCARhSDM.ZIBhSDM.ZIB.iCARhSDM.ZIB.iCAR.alterationhSDM.ZIPhSDM.ZIP.iCARhSDM.ZIP.iCAR.alterationinv.logitlogit
Citation
To cite package ‘hSDM’ in publications use:
Vieilledent G (2023). hSDM: Hierarchical Bayesian Species Distribution Models. R package version 1.4.4, https://CRAN.R-project.org/package=hSDM.
Corresponding BibTeX entry:
@Manual{,
title = {hSDM: Hierarchical Bayesian Species Distribution Models},
author = {Ghislain Vieilledent},
year = {2023},
note = {R package version 1.4.4},
url = {https://CRAN.R-project.org/package=hSDM},
}
Readme and manuals
hSDM R Package
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Installation
Install the latest stable version of hSDM from CRAN with:
install.packages("hSDM")
Or install the development version of hSDM from GitHub with:
devtools::install_github("ghislainv/hSDM")
Vignettes and manual
- Presentation at ISEC 2014: hSDM-ISEC2014.pdf
- Long vignette with several examples: hSDM-vignette.pdf
- Manual: hSDM-manual.pdf
In the wild
- Tutorial on using opportunistic species occurrence data for occupancy modelling by Adam M. Wilson on GitHub.
- Tutorial on modelling spatial autocorrelation by Jérôme Guélat on Amazonaws.
Contributing
The hSDM R package is Open Source and released under the GNU GPL version 3 license. Anybody who is interested can contribute to the package development following our Contributing guide. Every contributor must agree to follow the project's Code of conduct.
References
Diez J. M. and Pulliam H. R. 2007. Hierarchical analysis of species distributions and abundance across environmental gradients. Ecology. 88(12): 3144-3152.
Gelfand A. E., Silander J. A., Wu S. S., Latimer A., Lewis P. O., Rebelo A. G. and Holder M. 2006. Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis. 1(1): 41-92.
Latimer, A. M.; Wu, S. S.; Gelfand, A. E. & Silander, J. A. 2006. Building statistical models to analyze species distributions. Ecological Applications. 16(1): 33-50.
MacKenzie, D. I.; Nichols, J. D.; Lachman, G. B.; Droege, S.; Andrew Royle, J. and Langtimm, C. A. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 83: 2248-2255.
Royle, J. A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics. 60: 108-115.
