Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.

Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

Flexible nonlinear extension of spatial autoregressive (SAR),
    spatial error (SEM), and spatial autoregressive with autoregressive
    disturbances (SARAR) models with multiple regression engines
    (generalized additive models ('mgcv'), gradient boosting ('mboost'),
    multivariate adaptive regression splines ('earth'), and 'xgboost')
    and two families of spatial-parameter estimators: maximum likelihood
    and the determinant-free Closed-Form Estimator of Smirnov (2020)
    <doi:10.1111/gean.12268>. See Geniaux G. (2026). "Flexible nonlinear
    spatial autoregressive models: a gradient boosting approach with
    closed-form estimation." Presented at Spatial Econometrics World
    Congress (SEA/SEW 2026, Paris), unpublished.

Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.