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.

High-performance spatial species accumulation curves using
    nearest-neighbor algorithms. Implements 'kNN' and 'kNCN' sampling methods
    with a 'C++' backend for speed. Supports Hill numbers (q=0,1,2), beta
    diversity partitioning (turnover/nestedness), coverage-based rarefaction
    and extrapolation, phylogenetic diversity (Faith's PD, mean pairwise
    distance, mean nearest taxon distance), functional diversity accumulation,
    diversity-area relationships (DAR), endemism-area curves, sampling-effort
    correction and fragmentation analysis, and species-area relationship (SAR)
    models based on extreme value theory (EVT). Multiple starting points
    (seeds) provide uncertainty quantification.
    Methods are described in 'Chao' et al. (2014) <doi:10.1890/13-0133.1>,
    'Baselga' (2010) <doi:10.1111/j.1466-8238.2009.00490.x>,
    'Chao' and 'Jost' (2012) <doi:10.1890/11-1952.1>,
    'Faith' (1992) <doi:10.1016/0006-3207(92)91201-3>,
    'Ma' (2018) <doi:10.1002/ece3.4526>,
    'Borda-de-Agua' et al. (2025) <doi:10.1038/s41467-025-59239-7>,
    'Hanski' et al. (2013) <doi:10.1073/pnas.1311190110>,
    and 'Jost' (2007) <doi:10.1890/06-1736.1>.