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 (proportions bounded between 0 and 1). The package supports both non-spatial models and spatial models based on the Simultaneous Autoregressive (SAR) and Leroux Conditional Autoregressive (CAR) structures, with optional survey design effect (DEFF) adjustments. In addition, it provides utility functions for constructing spatial weights matrices and performing spatial autocorrelation diagnostics. The 'rjags' package is used to obtain posterior estimates via Markov Chain Monte Carlo (MCMC). For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Liu (2009) <https://api.drum.lib.umd.edu/server/api/core/bitstreams/cb8e2cbf-441e-4f0f-b4b3-6182f3cf24de/content>, 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>, 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>.

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