deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915662038827008 |
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| author | Vu, Quan Shao, Xuanjie Huser, Raphaël Zammit-Mangion, Andrew |
| author_facet | Vu, Quan Shao, Xuanjie Huser, Raphaël Zammit-Mangion, Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations Vu, Quan Shao, Xuanjie Huser, Raphaël Zammit-Mangion, Andrew Computation Methodology 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. |
| title | deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations |
| topic | Computation Methodology |
| url | https://arxiv.org/abs/2512.08137 |