An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.

Fits Bayesian hierarchical spatial process models for
    point-referenced Gaussian, Poisson, binomial, and binary data using stacking
    of predictive densities. It involves sampling from analytically available
    posterior distributions conditional upon some candidate values of the
    spatial process parameters and, subsequently assimilate inference from these
    individual posterior distributions using Bayesian predictive stacking. Our
    algorithm is highly parallelizable and hence, much faster than traditional
    Markov chain Monte Carlo algorithms while delivering competitive predictive
    performance. See Zhang, Tang, and Banerjee (2024)
    <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee
    (2024) <doi:10.48550/arXiv.2406.04655> for details.

A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X 
    regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references 
    Minguez, R., Lopez, F.A., and Mur, J. (2022) <doi:10.18637/jss.v104.i11>
    Mur, J., Lopez, F.A., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.