vmsae: Variational Multivariate Spatial Small Area Estimation

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:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	dplyr, ggplot2, gridExtra, sf, tidyr, reticulate, methods, rlang
Published:	2025-05-09
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 LICENSE
URL:	https://github.com/zhenhua-wang/vmsae
NeedsCompilation:	no
CRAN checks:	vmsae results

Documentation:

Reference manual:

vmsae.pdf

Downloads:

Package source:	vmsae_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): vmsae_0.1.0.tgz, r-oldrel (arm64): vmsae_0.1.0.tgz, r-release (x86_64): vmsae_0.1.0.tgz, r-oldrel (x86_64): vmsae_0.1.0.tgz

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