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deepspat  

Deep Compositional Spatial Models
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]
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 07 Mar. 2026.
Previous versions and publish date:
0.3.0 (2025-11-12 22:00)
Other packages that cited deepspat R package
View deepspat citation profile
Other R packages that deepspat depends, imports, suggests or enhances
Complete documentation for deepspat
Functions, R codes and Examples using the deepspat R package
Full deepspat package functions and examples
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