Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process
(GP) distributed spatial random effect, are widely used for analyses of geospatial data. We …

Feature selection to identify spatially variable genes or other biologically informative genes
is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose …

Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about
ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory …

Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …

Background The identification of genes that vary across spatial domains in tissues and cells
is an essential step for spatial transcriptomics data analysis. Given the critical role it serves …

Despite several attempts to compare and examine the predictive accuracy of real estate
sales and rent prices between the regression‐based and neural‐network (NN)‐based …

This paper describes and illustrates functionality of the spNNGP R package. The package
provides a suite of spatial regression models for Gaussian and non-Gaussian point …

Analysis of geospatial data has traditionally been model-based, with a mean model,
customarily specified as a linear regression on the covariates, and a Gaussian process …

For multivariate spatial Gaussian process models, customary specifications of cross-
covariance functions do not exploit relational inter-variable graphs to ensure process-level …

Recently, addressing spatial confounding has become a major topic in spatial statistics.
However, the literature has provided conflicting definitions, and many proposed definitions …