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:Ghislain Vieilledent [aut, cre], Matthieu Autier [ctb], Alan E. Gelfand [ctb], Jérôme Guélat [ctb], Marc Kéry [ctb], Andrew M. Latimer [ctb], Cory Merow [ctb], Frédéric Mortier [ctb], John A. Silander Jr. [ctb], Adam M. Wilson [ctb], Shanshan Wu [ctb], CIRAD [cph, fnd]

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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ghislainv/hsdm/issues

Uses libs:
  • gsl– GNU Scientific Library (GSL)
Datasets:

4.39 score 2 stars 41 scripts 246 downloads 3 mentions 17 exports 2 dependencies

Last updated 1 years agofrom:23c17415d9. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 24 2024
R-4.5-linux-x86_64OKNov 24 2024

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

Dependencies:codalattice

Introduction to hSDM

Rendered fromhSDM.Rmdusingknitr::rmarkdownon Nov 24 2024.

Last update: 2023-05-25
Started: 2019-05-12

Publications

Rendered frompublications.Rmdusingknitr::rmarkdownon Nov 24 2024.

Last update: 2023-05-25
Started: 2019-05-12

Readme and manuals

Help Manual

Help pageTopics
hierarchical Bayesian species distribution modelshSDM-package hSDM
Virtual altitudinal dataaltitude
Environmental data for South Africa's Cap Floristic Regioncfr.env
Count data for the Willow tit (from Kéry and Royle 2010)data.Kery2010
Data of presence-absence (from Latimer et al. 2006)datacells.Latimer2006
Counts of the number of frogs in a water bodyfrogs
Binomial logistic regression modelhSDM.binomial
Binomial logistic regression model with CAR processhSDM.binomial.iCAR
N-mixture modelhSDM.Nmixture
N-mixture model with CAR processhSDM.Nmixture.iCAR
N-mixture model with K, the maximal theoretical abundancehSDM.Nmixture.K
Poisson log regression modelhSDM.poisson
Poisson log regression model with CAR processhSDM.poisson.iCAR
Site occupancy modelhSDM.siteocc
Site-occupancy model with CAR processhSDM.siteocc.iCAR
ZIB (Zero-Inflated Binomial) modelhSDM.ZIB
ZIB (Zero-Inflated Binomial) model with CAR processhSDM.ZIB.iCAR
ZIB (Zero-Inflated Binomial) model with CAR process taking into account site alterationhSDM.ZIB.iCAR.alteration
ZIP (Zero-Inflated Poisson) modelhSDM.ZIP
ZIP (Zero-Inflated Poisson) model with CAR processhSDM.ZIP.iCAR
ZIP (Zero-Inflated Poisson) model with CAR process taking into account site alterationhSDM.ZIP.iCAR.alteration
Generalized logit and inverse logit functioninv.logit logit
Neighborhood data (from Latimer et al. 2006)neighbors.Latimer2006
Predict method for models fitted with hSDMpredict.hSDM
Occurrence data for _Protea punctata_ Meisn. in the Cap Floristic Regionpunc10