Univariate and Multivariate Spatial Modeling of Species Abundance
Function for Fitting Univariate Abundance GLMMs
Count data for six warbler species in Pennsylvania, USA
Covariates and coordinates for prediction of relative warbler abundanc...
Simulated repeated count data of 6 species across 225 sites
Function for Fitting Single-Species Hierarchical Distance Sampling Mod...
Extract Model Fitted Values for abund Object
Extract Model Fitted Values for DS Object
Extract Model Fitted Values for lfMsAbund Object
Extract Model Fitted Values for lfMsDS Object
Extract Model Fitted Values for lfMsNMix Object
Extract Model Fitted Values for msAbund Object
Extract Model Fitted Values for msDS Object
Extract Model Fitted Values for msNMix Object
Extract Model Fitted Values for NMix Object
Extract Model Fitted Values for sfMsAbund Object
Extract Model Fitted Values for sfMsDS Object
Extract Model Fitted Values for sfMsNMix Object
Extract Model Fitted Values for spAbund Object
Extract Model Fitted Values for spDS Object
Extract Model Fitted Values for spNMix Object
Extract Model Fitted Values for svcAbund Object
Extract Model Fitted Values for svcMsAbund Object
Count data of 12 foliage gleaning bird species in 2015 in the Hubbard ...
Function for Fitting Latent Factor Multivariate Abundance GLMMs
Function for Fitting Latent Factor Multi-Species Hierarchical Distance...
Function for Fitting Latent Factor Multi-species N-mixture Models
Function for Fitting Multivariate Abundance GLMMs
Function for Fitting Multi-Species Hierarchical Distance Sampling Mode...
Function for Fitting Multi-species N-mixture Models
Distance sampling data of 16 bird species observed in the Disney Wilde...
Land cover covariates and coordinates at a 1ha resolution across Disne...
Function for Fitting Single-Species N-mixture Models
Function for performing posterior predictive checks
Function for prediction at new locations for univariate GLMMs
Function for prediction at new locations for single-species hierarchic...
Function for prediction at new locations for latent factor multivariat...
Function for prediction at new locations for latent factor multi-speci...
Function for prediction at new locations for latent factor multi-speci...
Function for prediction at new locations for multivariate GLMMs
Function for prediction at new locations for multi-species hierarchica...
Function for prediction at new locations for multi-species N-mixture m...
Function for prediction at new locations for single-species N-mixture ...
Function for prediction at new locations for spatial factor multivaria...
Function for prediction at new locations for spatial factor multi-spec...
Function for prediction at new locations for spatial factor multi-spec...
Function for prediction at new locations for univariate spatial GLMMs
Function for prediction at new locations for single-species spatially-...
Function for prediction at new locations for single-species spatial N-...
Function for prediction at new locations for univariate Gaussian spati...
Function for prediction at new locations for multivariate spatially-va...
Function for Fitting Spatial Factor Multivariate Abundance GLMMs
Function for Fitting Spatial Factor Multi-Species Hierarchical Distanc...
Function for Fitting Spatial Factor Multi-species N-mixture Models
Simulate Univariate Data for Testing GLMMs
Simulate Single-Species Distance Sampling Data
Simulate Multivariate Data for Testing GLMMs
Simulate Multi-Species Distance Sampling Data
Simulate Multi-Species Repeated Count Data with Imperfect Detection
Simulate Single-Species Count Data with Imperfect Detection
Function for Fitting Univariate Spatial Abundance GLMs
Function for Fitting Single-Species Spatially-Explicit Hierarchical Di...
Function for Fitting Single-Species Spatial N-Mixture Models
Methods for abund Object
Methods for DS Object
Methods for lfMsAbund Object
Methods for lfMsDS Object
Methods for lfMsNMix Object
Methods for msAbund Object
Methods for msDS Object
Methods for msNMix Object
Methods for NMix Object
Methods for sfMsAbund Object
Methods for sfMsDS Object
Methods for sfMsNMix Object
Methods for spAbund Object
Methods for spDS Object
Methods for spNMix Object
Methods for svcAbund Object
Methods for svcMsAbund Object
Function for Fitting Univariate Spatialy-Varying Coefficient GLMMs
Function for Fitting Spatially-Varying Coefficient Multivariate Abunda...
Compute Widely Applicable Information Criterion for spAbundance Model ...
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.
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