Package: spAbundance 0.2.0

spAbundance: Univariate and Multivariate Spatial Modeling of Species Abundance

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 (2022) <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.

Authors:Jeffrey Doser [aut, cre], Andrew Finley [aut]

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spAbundance.pdf |spAbundance.html✨
spAbundance/json (API)
NEWS

# Install 'spAbundance' in R:
install.packages('spAbundance', repos = c('https://doserjef.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/doserjef/spabundance/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • bbsData - Count data for six warbler species in Pennsylvania, USA
  • bbsPredData - Covariates and coordinates for prediction of relative warbler abundance in Pennsylvania, USA
  • dataNMixSim - Simulated repeated count data of 6 species across 225 sites
  • hbefCount2015 - Count data of 12 foliage gleaning bird species in 2015 in the Hubbard Brook Experimental Forest
  • neonDWP - Distance sampling data of 16 bird species observed in the Disney Wilderness Preserve in 2018 in Florida, USA
  • neonPredData - Land cover covariates and coordinates at a 1ha resolution across Disney Wilderness Preserve

On CRAN:

25 exports 15 stars 2.58 score 17 dependencies 1 dependents 44 scripts 391 downloads

Last updated 6 days agofrom:f64d08ea7f. Checks:OK: 8 ERROR: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 11 2024
R-4.5-win-x86_64OKSep 11 2024
R-4.5-linux-x86_64OKSep 11 2024
R-4.4-win-x86_64OKSep 11 2024
R-4.4-mac-x86_64OKSep 11 2024
R-4.4-mac-aarch64OKSep 11 2024
R-4.3-win-x86_64ERRORSep 11 2024
R-4.3-mac-x86_64OKSep 11 2024
R-4.3-mac-aarch64OKSep 11 2024

Exports:abundDSlfMsAbundlfMsDSlfMsNMixmsAbundmsDSmsNMixNMixppcAbundsfMsAbundsfMsDSsfMsNMixsimAbundsimDSsimMsAbundsimMsDSsimMsNMixsimNMixspAbundspDSspNMixsvcAbundsvcMsAbundwaicAbund

Dependencies:abindbootcodacodetoolsdoParallelforeachiteratorslatticelme4MASSMatrixminqanlmenloptrRANNRcppRcppEigen

Readme and manuals

Help Manual

Help pageTopics
Function for Fitting Univariate Abundance GLMMsabund
Count data for six warbler species in Pennsylvania, USAbbsData
Covariates and coordinates for prediction of relative warbler abundance in Pennsylvania, USAbbsPredData
Simulated repeated count data of 6 species across 225 sitesdataNMixSim
Function for Fitting Single-Species Hierarchical Distance Sampling ModelsDS
Extract Model Fitted Values for abund Objectfitted.abund
Extract Model Fitted Values for DS Objectfitted.DS
Extract Model Fitted Values for lfMsAbund Objectfitted.lfMsAbund
Extract Model Fitted Values for lfMsDS Objectfitted.lfMsDS
Extract Model Fitted Values for lfMsNMix Objectfitted.lfMsNMix
Extract Model Fitted Values for msAbund Objectfitted.msAbund
Extract Model Fitted Values for msDS Objectfitted.msDS
Extract Model Fitted Values for msNMix Objectfitted.msNMix
Extract Model Fitted Values for NMix Objectfitted.NMix
Extract Model Fitted Values for sfMsAbund Objectfitted.sfMsAbund
Extract Model Fitted Values for sfMsDS Objectfitted.sfMsDS
Extract Model Fitted Values for sfMsNMix Objectfitted.sfMsNMix
Extract Model Fitted Values for spAbund Objectfitted.spAbund
Extract Model Fitted Values for spDS Objectfitted.spDS
Extract Model Fitted Values for spNMix Objectfitted.spNMix
Extract Model Fitted Values for svcAbund Objectfitted.svcAbund
Extract Model Fitted Values for svcMsAbund Objectfitted.svcMsAbund
Count data of 12 foliage gleaning bird species in 2015 in the Hubbard Brook Experimental ForesthbefCount2015
Function for Fitting Latent Factor Multivariate Abundance GLMMslfMsAbund
Function for Fitting Latent Factor Multi-Species Hierarchical Distance Sampling ModelslfMsDS
Function for Fitting Latent Factor Multi-species N-mixture ModelslfMsNMix
Function for Fitting Multivariate Abundance GLMMsmsAbund
Function for Fitting Multi-Species Hierarchical Distance Sampling ModelsmsDS
Function for Fitting Multi-species N-mixture ModelsmsNMix
Distance sampling data of 16 bird species observed in the Disney Wilderness Preserve in 2018 in Florida, USAneonDWP
Land cover covariates and coordinates at a 1ha resolution across Disney Wilderness PreserveneonPredData
Function for Fitting Single-Species N-mixture ModelsNMix
Function for performing posterior predictive checksppcAbund
Function for prediction at new locations for univariate GLMMspredict.abund
Function for prediction at new locations for single-species hierarchical distance sampling modelspredict.DS
Function for prediction at new locations for latent factor multivariate GLMMspredict.lfMsAbund
Function for prediction at new locations for latent factor multi-species hierarchical distance sampling modelspredict.lfMsDS
Function for prediction at new locations for latent factor multi-species N-mixture modelspredict.lfMsNMix
Function for prediction at new locations for multivariate GLMMspredict.msAbund
Function for prediction at new locations for multi-species hierarchical distance sampling modelspredict.msDS
Function for prediction at new locations for multi-species N-mixture modelspredict.msNMix
Function for prediction at new locations for single-species N-mixture modelspredict.NMix
Function for prediction at new locations for spatial factor multivariate GLMMspredict.sfMsAbund
Function for prediction at new locations for spatial factor multi-species hierarchical distance sampling modelspredict.sfMsDS
Function for prediction at new locations for spatial factor multi-species N-mixture modelspredict.sfMsNMix
Function for prediction at new locations for univariate spatial GLMMspredict.spAbund
Function for prediction at new locations for single-species spatially-explicit hierarchical distance sampling modelspredict.spDS
Function for prediction at new locations for single-species spatial N-mixture modelspredict.spNMix
Function for prediction at new locations for univariate Gaussian spatially-varying coefficient modelspredict.svcAbund
Function for prediction at new locations for multivariate spatially-varying coefficient GLMMspredict.svcMsAbund
Function for Fitting Spatial Factor Multivariate Abundance GLMMssfMsAbund
Function for Fitting Spatial Factor Multi-Species Hierarchical Distance Sampling ModelssfMsDS
Function for Fitting Spatial Factor Multi-species N-mixture ModelssfMsNMix
Simulate Univariate Data for Testing GLMMssimAbund
Simulate Single-Species Distance Sampling DatasimDS
Simulate Multivariate Data for Testing GLMMssimMsAbund
Simulate Multi-Species Distance Sampling DatasimMsDS
Simulate Multi-Species Repeated Count Data with Imperfect DetectionsimMsNMix
Simulate Single-Species Count Data with Imperfect DetectionsimNMix
Function for Fitting Univariate Spatial Abundance GLMsspAbund
Function for Fitting Single-Species Spatially-Explicit Hierarchical Distance Sampling ModelsspDS
Function for Fitting Single-Species Spatial N-Mixture ModelsspNMix
Methods for abund Objectplot.abund print.abund summary.abund
Methods for DS Objectplot.DS print.DS summary.DS
Methods for lfMsAbund Objectplot.lfMsAbund print.lfMsAbund summary.lfMsAbund
Methods for lfMsDS Objectplot.lfMsDS print.lfMsDS summary.lfMsDS
Methods for lfMsNMix Objectplot.lfMsNMix print.lfMsNMix summary.lfMsNMix
Methods for msAbund Objectplot.msAbund print.msAbund summary.msAbund
Methods for msDS Objectplot.msDS print.msDS summary.msDS
Methods for msNMix Objectplot.msNMix print.msNMix summary.msNMix
Methods for NMix Objectplot.NMix print.NMix summary.NMix
Methods for sfMsAbund Objectplot.sfMsAbund print.sfMsAbund summary.sfMsAbund
Methods for sfMsDS Objectplot.sfMsDS print.sfMsDS summary.sfMsDS
Methods for sfMsNMix Objectplot.sfMsNMix print.sfMsNMix summary.sfMsNMix
Methods for spAbund Objectplot.spAbund print.spAbund summary.spAbund
Methods for spDS Objectplot.spDS print.spDS summary.spDS
Methods for spNMix Objectplot.spNMix print.spNMix summary.spNMix
Methods for svcAbund Objectplot.svcAbund print.svcAbund summary.svcAbund
Methods for svcMsAbund Objectplot.svcMsAbund print.svcMsAbund summary.svcMsAbund
Function for Fitting Univariate Spatialy-Varying Coefficient GLMMssvcAbund
Function for Fitting Spatially-Varying Coefficient Multivariate Abundance GLMMssvcMsAbund
Compute Widely Applicable Information Criterion for spAbundance Model ObjectswaicAbund