Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE'
	R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, 
	such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial 
	interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and 
	differential expression analysis using spatial mixed models.

Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) <doi:10.48550/arXiv.2110.07051>.

Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().

Fast computation of Conley (1999) <doi:10.1016/S0304-4076(98)00084-0>
    spatial heteroskedasticity and autocorrelation consistent (HAC) standard
    errors for linear regression models with geo-coded data, with a fast C++
    implementation by Christensen, Hartman, and Samii (2021)
    <doi:10.1017/S0020818321000187>. Performance-critical distance calculations,
    kernel weighting, and variance component accumulation are implemented in C++
    via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial
    correlation range from covariograms and correlograms following the bandwidth
    selection method proposed in Lehner (2026) <doi:10.48550/arXiv.2603.03997>,
    and diagnostic visualizations for bandwidth selection.