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deepspat
View on CRAN: Click
here
Download and install deepspat package within the R console
Install from CRAN:
install.packages("deepspat")
Install from Github:
library("remotes")
install_github("cran/deepspat") Install by package version:
library("remotes")
install_version("deepspat", "0.3.1") Attach the package and use:
library("deepspat")
Maintained by
Quan Vu
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-11-12
Latest Update: 2025-11-12
Description:
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>.
How to cite:
Quan Vu (2025). deepspat: Deep Compositional Spatial Models. R package version 0.3.1, https://cran.r-project.org/web/packages/deepspat. Accessed 17 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:31), 0.3.0 (2025-11-12 22:00)
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