Other packages > Find by keyword >

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]
All associated links for this package
First Published: 2023-10-20
Latest Update: 2024-10-05
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 15 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:06), 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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

objectSignals  
Observer Pattern for S4
A mutable Signal object can report changes to its state, clients could register functions so that t ...
Download / Learn more Package Citations See dependency  
SiER  
Signal Extraction Approach for Sparse Multivariate Response Regression
Methods for regression with high-dimensional predictors andunivariate or maltivariate response varia ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
functClust  
Functional Clustering of Redundant Components of a System
Cluster together the components that make up an interactivesystem on the basis of their functional r ...
Download / Learn more Package Citations See dependency  
RXshrink  
Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression
Functions are provided to calculate and display ridge TRACE Diagnostics for a variety of alternativ ...
Download / Learn more Package Citations See dependency  
CTTinShiny  
Shiny Interface for the CTT Package
A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA