The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …

We consider alternate formulations of recently proposed hierarchical nearest neighbor
Gaussian process (NNGP) models for improved convergence, faster computing time, and …

Variational approximations to Gaussian processes (GPs) typically use a small set of
inducing points to form a low-rank approximation to the covariance matrix. In this work, we …

The ongoing global change and the increased interest in macroecological processes call for
the analysis of spatially extensive data on species communities to understand and forecast …

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 …

Determining the spatial distributions of species and communities is a key task in ecology
and conservation efforts. Joint species distribution models are a fundamental tool in …

We introduce a class of scalable Bayesian hierarchical models for the analysis of massive
geostatistical datasets. The underlying idea combines ideas on high-dimensional …

Atmospheric CO2 concentrations are dependent on land-atmosphere carbon fluxes
resultant from forest dynamics and land-use changes. These fluxes are not well-constrained …

Numerous modelling techniques exist to estimate abundance of plant and animal
populations. The most accurate methods account for multiple complexities found in …

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 …