doi:10.48550/arXiv.2503.14710>. The 'vmsae' package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.">

vmsae: Variational Multivariate Spatial Small Area Estimation (original) (raw)

Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using 'NumPyro' and 'PyTorch' backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The 'vmsae' package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: dplyr, ggplot2, gridExtra, sf, tidyr, reticulate, methods, rlang
Published: 2025-10-08
DOI: 10.32614/CRAN.package.vmsae
Author: Zhenhua Wang [aut, cre], Paul A. Parker [aut, res], Scott H. Holan [aut, res]
Maintainer: Zhenhua Wang <zhenhua.wang at missouri.edu>
BugReports: https://github.com/zhenhua-wang/vmsae/issues
License: MIT + file
URL: https://github.com/zhenhua-wang/vmsae
NeedsCompilation: no
CRAN checks: vmsae results

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