Package: saeHB.Spatial.Beta 0.1.0

saeHB.Spatial.Beta: Small Area Estimation Hierarchical Bayes for Spatial Beta Model

Provides several functions and datasets for area-level Small Area Estimation using the Hierarchical Bayesian (HB) method. Model-based estimators are designed for variables of interest that follow a Beta distribution. The package supports spatial structures under the Simultaneous Autoregressive (SAR) and Leroux Conditional Autoregressive (CAR) models, accommodating survey design effect (DEFF) adjustments. The 'rjags' package is employed to obtain parameter estimates via Gibbs Sampling. For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Kubacki and Jedrzejczak (2016) <doi:10.59170/stattrans-2016-022>, Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>, and Chung and Datta (2020) <https://www.census.gov/content/dam/Census/library/working-papers/2020/adrm/RRS2020-07.pdf>.

Authors:Boby Iwan [aut, cre], Cucu Sumarni [aut]

saeHB.Spatial.Beta_0.1.0.tar.gz
saeHB.Spatial.Beta_0.1.0.zip(r-4.7)saeHB.Spatial.Beta_0.1.0.zip(r-4.6)saeHB.Spatial.Beta_0.1.0.zip(r-4.5)
saeHB.Spatial.Beta_0.1.0.tgz(r-4.6-any)saeHB.Spatial.Beta_0.1.0.tgz(r-4.5-any)
saeHB.Spatial.Beta_0.1.0.tar.gz(r-4.7-any)saeHB.Spatial.Beta_0.1.0.tar.gz(r-4.6-any)
saeHB.Spatial.Beta_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
saeHB.Spatial.Beta/json (API)

# Install 'saeHB.Spatial.Beta' in R:

install.packages('saeHB.Spatial.Beta', repos = c('https://bobyiwan.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bobyiwan/saehb.spatial.beta/issues

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

adjacency_mat - Binary Adjacency Matrix
databeta - Synthetic Data for Small Area Estimation using Spatial Beta Model
databeta_na - Synthetic Data with Missing Values for Small Area Estimation
weight_mat - Row-Standardized Spatial Weight Matrix

On CRAN:

jags cpp

4.00 score 8 exports 20 dependencies

Last updated from:4dc537d94e. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	159
source / vignettes	OK	301
linux-release-x86_64	WARNING	152
macos-release-arm64	WARNING	240
macos-oldrel-arm64	WARNING	201
windows-devel	WARNING	98
windows-release	WARNING	84
windows-oldrel	WARNING	90
wasm-release	OK	171

Exports:beta_lerouxcar beta_nonspatial beta_sar betadeff_lerouxcar betadeff_nonspatial betadeff_sar build_w moran_test

Dependencies:boot class classInt coda DBI deldir e1071 KernSmooth lattice MASS proxy Rcpp rjags s2 sf sp spData spdep units wk

Getting Started with saeHB.Spatial.Beta

Rendered fromsaeHB_Spatial_Beta.Rmdusingknitr::rmarkdown

Last update: 2026-06-13
Started: 2026-06-09

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Binary Adjacency Matrix	adjacency_mat
Small Area Estimation using Hierarchical Bayesian Method under Spatial Beta-Leroux CAR Model	beta_lerouxcar
Small Area Estimation using Hierarchical Bayesian Method under Non-Spatial Beta Model	beta_nonspatial
Small Area Estimation using Hierarchical Bayesian Method under Spatial Beta SAR Model	beta_sar
Small Area Estimation using Hierarchical Bayesian Method under Spatial Beta-Leroux CAR Model with Design Effect	betadeff_lerouxcar
Small Area Estimation using Hierarchical Bayesian Method under Non-Spatial Beta Model with Design Effect	betadeff_nonspatial
Small Area Estimation using Hierarchical Bayesian Method under Spatial Beta SAR Model with Design Effect	betadeff_sar
Build Spatial Weights Matrix	build_w
Synthetic Data for Small Area Estimation using Spatial Beta Model	databeta
Synthetic Data with Missing Values for Small Area Estimation	databeta_na
Moran's I Test for Spatial Autocorrelation	moran_test
Row-Standardized Spatial Weight Matrix	weight_mat