deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations

Quan Vu; Xuanjie Shao; Raphaël Huser; Andrew Zammit-Mangion

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arXiv · 2512.08137

deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations

Quan Vu·Xuanjie Shao·Raphaël Huser·Andrew Zammit-Mangion

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Abstract

Nonstationarity in spatial and spatio-temporal processes is ubiquitous in environmental datasets, but is not often addressed in practice, due to a scarcity of statistical software packages that implement nonstationary models. In this article, we introduce the R software package deepspat, which allows for modeling, fitting and prediction with nonstationary spatial and spatio-temporal models applied to Gaussian and extremes data. The nonstationary models in our package are constructed using a deep multi-layered deformation of the original spatial or spatio-temporal domain, and are straightforward to implement. Model parameters are estimated using gradient-based optimization of customized loss functions with tensorflow, which implements automatic differentiation. The functionalities of the package are illustrated through simulation studies and an application to Nepal temperature data.

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Quan Vu, Xuanjie Shao, Raphaël Huser, Andrew Zammit-Mangion. 2025-12-09. deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations. https://arxiv.org/abs/2512.08137

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