vmsae0.1.2 package

Variational Multivariate Spatial Small Area Estimation

coef-VGMSFH-method

Extract Coefficients from a VGMSFH Object

confint-VGMSFH-method

Compute Credible Intervals for VGMSFH Parameters

Decoder-class

Decoder S4 Class

download_pretrained_vae

Download and Extract a Pretrained VAE Model

install_environment

Install python environment.

load_environment

Load Python Environment and Source Model Modules

load_vae

Load Pretrained VAE Decoder

plot-VGMSFH-method

Plot VGMSFH Result

summary-VGMSFH-method

Summarize VGMSFH Result

train_vae

Train VAE for CAR Prior

vgmsfh_numpyro

Run VGMSFH Using NumPyro

VGMSFH-class

VGMSFH S4 Class

Download source packageRead PDF manual

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.

  • Maintainer: Zhenhua Wang
  • License: MIT + file LICENSE
  • Last published: 2025-10-08

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