Bayesian Spatial and Space-Time Linear Mixed Models // Fits Bayesian linear mixed models for spatial and space-time data with fixed effects, independent and identically distributed (iid) grouped random effects, and structured latent processes. The formula interface supports first-order autoregressive (AR(1)) effects, dense Gaussian processes, nearest-neighbor Gaussian processes, proper and Leroux conditional autoregressive (CAR) effects, ordered directed acyclic graph autoregressive (DAGAR) effects, separable CAR-time and DAGAR- time effects, and spatially varying coefficients. The sampler uses sparse precision matrix calculations when available and includes post-fitting tools for latent process recovery, fitted values, prediction, pointwise log likelihoods, and posterior sample extraction. Method details include Datta et al. (2016) doi:10.1080/01621459.2015.1044091, Finley et al. (2019) doi:10.1080/10618600.2018.1537924, Datta et al. (2019) doi:10.1214/19-BA1177, and May and Finley (2025) doi:10.1016/j.spasta.2025.100917.