Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. For further details, see Murakami et al. (2025) <doi:10.48550/arXiv.2510.00968>.

Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.

Computes the effective range of a smoothing matrix, 
    which is a measure of the distance to which smoothing occurs. 
    This is motivated by the application of spatial splines for adjusting 
    for unmeasured spatial confounding in regression models, but the
    calculation of effective range can be applied to smoothing 
    matrices in other contexts. For algorithmic details, see Rainey and Keller
    (2024) "spconfShiny: an R Shiny application..." <doi:10.1371/journal.pone.0311440>
    and Keller and Szpiro (2020)
    "Selecting a Scale for Spatial Confounding Adjustment" <doi:10.1111/rssa.12556>.

Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means. See Walvoort et al. (2010) 
        <doi:10.1016/j.cageo.2010.04.005> for details.

Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.

Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics
    by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.