Decoder-class

An S4 class to represent a neural network decoder, used for emulating spatial priors.
The class includes parameters for input and output weight matrices and biases, as well as region identifiers.

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.

Zhenhua Wang

vmsae

Variational Multivariate Spatial Small Area Estimation

Paul A. Parker

Scott H. Holan

Decoder-class function

<dl>
<dt><code>GEOID</code></dt>
<dd>A character vector of region or area identifiers.</dd></dl>
<dt><code>W_in</code></dt>
<dd>An array representing the input weight matrix of the decoder.</dd>
<dt><code>B_in</code></dt>
<dd>An array representing the input bias vector of the decoder.</dd>
<dt><code>W_out</code></dt>
<dd>An array representing the output weight matrix of the decoder.</dd>
<dt><code>B_out</code></dt>
<dd>An array representing the output bias vector of the decoder.</dd>

Slots

Decoder S4 Class — Decoder-class

<dl>
<dt><code>GEOID</code></dt>
<dd>A character vector of region or area identifiers.</dd>


<dt><code>W_in</code></dt>
<dd>An array representing the input weight matrix of the decoder.</dd>


<dt><code>B_in</code></dt>
<dd>An array representing the input bias vector of the decoder.</dd>


<dt><code>W_out</code></dt>
<dd>An array representing the output weight matrix of the decoder.</dd>


<dt><code>B_out</code></dt>
<dd>An array representing the output bias vector of the decoder.</dd>


</dl>

Decoder-class: Decoder S4 Class

Description

Arguments

Slots