deepspat: Deep Compositional Spatial Models
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>, and
Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>.
| Version: |
0.3.0 |
| Imports: |
data.table, dplyr, Matrix, methods, reticulate, keras, tensorflow, tfprobability, evd, SpatialExtremes, fields |
| Published: |
2025-11-12 |
| DOI: |
10.32614/CRAN.package.deepspat (may not be active yet) |
| Author: |
Andrew Zammit-Mangion [aut],
Quan Vu [aut, cre],
Xuanjie Shao [aut] |
| Maintainer: |
Quan Vu <quanvustats at gmail.com> |
| License: |
Apache License 2.0 |
| NeedsCompilation: |
no |
| SystemRequirements: |
TensorFlow (https://www.tensorflow.org/), |
| Materials: |
README, NEWS |
| CRAN checks: |
deepspat results |
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