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spAbundance  

Univariate and Multivariate Spatial Modeling of Species Abundance
View on CRAN: Click here


Download and install spAbundance package within the R console
Install from CRAN:
install.packages("spAbundance")

Install from Github:
library("remotes")
install_github("cran/spAbundance")

Install by package version:
library("remotes")
install_version("spAbundance", "0.2.1")



Attach the package and use:
library("spAbundance")
Maintained by
Jeffrey Doser
[Scholar Profile | Author Map]
First Published: 2023-10-20
Latest Update: 2023-10-20
Description:
Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2020) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.
How to cite:
Jeffrey Doser (2023). spAbundance: Univariate and Multivariate Spatial Modeling of Species Abundance. R package version 0.2.1, https://cran.r-project.org/web/packages/spAbundance. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.1.0 (2023-10-20 12:20), 0.1.1 (2024-03-22 23:00), 0.1.3 (2024-04-08 21:30), 0.2.0 (2024-09-26 23:00)
Other packages that cited spAbundance R package
View spAbundance citation profile
Other R packages that spAbundance depends, imports, suggests or enhances
Complete documentation for spAbundance
Functions, R codes and Examples using the spAbundance R package
Some associated functions: DS . NMix . abund . bbsData.rda . bbsPredData.rda . dataNMixSim.rda . fitted.DS . fitted.NMix . fitted.abund . fitted.lfMsAbund . fitted.lfMsDS . fitted.lfMsNMix . fitted.msAbund . fitted.msDS . fitted.msNMix . fitted.sfMsAbund . fitted.sfMsDS . fitted.sfMsNMix . fitted.spAbund . fitted.spDS . fitted.spNMix . fitted.svcAbund . fitted.svcMsAbund . hbefCount2015.rda . lfMsAbund . lfMsDS . lfMsNMix . msAbund . msDS . msNMix . neonDWP.rda . neonPredData.rda . ppcAbund . predict.DS . predict.NMix . predict.abund . predict.lfMsAbund . predict.lfMsDS . predict.lfMsNMix . predict.msAbund . predict.msDS . predict.msNMix . predict.sfMsAbund . predict.sfMsDS . predict.sfMsNMix . predict.spAbund . predict.spDS . predict.spNMix . predict.svcAbund . predict.svcMsAbund . sfMsAbund . sfMsDS . sfMsNMix . simAbund . simDS . simMsAbund . simMsDS . simMsNMix . simNMix . spAbund . spDS . spNMix . summary.DS . summary.NMix . summary.abund . summary.lfMsAbund . summary.lfMsDS . summary.lfMsNMix . summary.msAbund . summary.msDS . summary.msNMix . summary.sfMsAbund . summary.sfMsDS . summary.sfMsNMix . summary.spAbund . summary.spDS . summary.spNMix . summary.svcAbund . summary.svcMsAbund . svcAbund . svcMsAbund . waicAbund . 
Some associated R codes: DS.R . NMix.R . abund.R . abundGaussian.R . generics.R . idist.R . lfMsAbund.R . lfMsAbundGaussian.R . lfMsDS.R . lfMsNMix.R . mkMatUtil.R . mkSpCov.R . msAbund.R . msAbundGaussian.R . msDS.R . msNMix.R . nn.R . plot-generics.R . ppcAbund.R . sfMsAbund.R . sfMsAbundGaussian.R . sfMsDS.R . sfMsNMix.R . simAbund.R . simDS.R . simMsAbund.R . simMsDS.R . simMsGaussian.R . simMsNMix.R . simNMix.R . spAbund.R . spAbundGaussian.R . spDS.R . spNMix.R . svcAbund.R . svcMsAbund.R . waicAbund.R .  Full spAbundance package functions and examples
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