Small Area Estimation Hierarchical Bayes for Spatial Beta Model R package saeHB.Spatial.Beta version 0.2.0 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 (proportions bounded between 0 and 1). The package supports both non-spatial and spatial models based on Simultaneous Autoregressive (SAR) and Leroux Conditional Autoregressive (CAR) structures for area-level random effects, with optional survey design effect (DEFF) adjustments for sampling variances. In addition, it provides utility functions for constructing spatial weights matrices and performing spatial autocorrelation diagnostics. The 'runjags' package is used to obtain posterior estimates via Markov Chain Monte Carlo (MCMC) with parallel computing capabilities. For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Liu et al. (2014) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2014001/article/14030-eng.pdf>, Kubacki and Jedrzejczak (2016) <doi:10.59170/stattrans-2016-022>, Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>, Chung and Datta (2020) <https://www.census.gov/content/dam/Census/library/working-papers/2020/adrm/RRS2020-07.pdf>, Figueroa-Zúñiga et al. (2013) <doi:10.1016/j.csda.2012.12.002>, Denwood (2016) <doi:10.18637/jss.v071.i09>, Anselin (1988) <doi:10.1007/978-94-015-7799-1>, and Anselin and Morrison (2019) <https://spatialanalysis.github.io/lab_tutorials/Spatial_Weights_as_Distance_Functions.html>. Software Depends: R (>= 4.1.0) Imports: runjags, coda, stats, grDevices, graphics, sf, spdep Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2 Boby Iwan <bobyiwanboby2122@gmail.com> Comprehensive R Archive Network (CRAN) Boby Iwan [aut, cre], Cucu Sumarni [aut] GPL-3 2026-09-20 application/tgz https://CRAN.R-project.org/package=saeHB.Spatial.Beta doi:10.32614/CRAN.package.saeHB.Spatial.Beta