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vmsae (version 0.1.2)

Variational Multivariate Spatial Small Area Estimation

Description

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) . 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.

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Install

install.packages('vmsae')

Monthly Downloads

211

Version

0.1.2

License

MIT + file LICENSE

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Maintainer

Zhenhua Wang

Last Published

October 8th, 2025

Functions in vmsae (0.1.2)

load_vae

Load Pretrained VAE Decoder
VGMSFH-class

VGMSFH S4 Class
Decoder-class

Decoder S4 Class
load_environment

Load Python Environment and Source Model Modules
summary,VGMSFH-method

Summarize VGMSFH Result
download_pretrained_vae

Download and Extract a Pretrained VAE Model
plot,VGMSFH-method

Plot VGMSFH Result
install_environment

Install python environment.
train_vae

Train VAE for CAR Prior
vgmsfh_numpyro

Run VGMSFH Using NumPyro
confint,VGMSFH-method

Compute Credible Intervals for VGMSFH Parameters
coef,VGMSFH-method

Extract Coefficients from a VGMSFH Object