deepspat
0.3.1Deep Compositional Spatial Models
Overview
Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) doi:10.1080/01621459.2021.1887741, Vu et al. (2022) doi:10.5705/ss.202020.0156, Vu et al. (2023) doi:10.1016/j.spasta.2023.100742, and Shao et al. (2025) doi:10.48550/arXiv.2505.12548.
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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2 releases. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.3.1Latest
- 0.3.02025-11-12
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-11-12
- Total releases
- 2 / 1 yrs
- License
- Apache License 2.0 OSI
- Download size
- 81 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet