Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit <https://sae4health.stat.uw.edu/>. Methodological details can be found at Wu et al. (2025) <doi:10.48550/arXiv.2505.01467>.

Select best combination of auxiliary variables with certain criterion.

Estimates the parameter of small area in binary data without
             auxiliary variable using Empirical Bayes technique, mainly from Rao 
             and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". 
             This package provides another option of direct estimation using weight.
             This package also features alpha and beta parameter estimation on calculating process of small area.
             Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> 
             and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.

Provides functionality to fit a zero-inflated estimator for small area estimation.
    This estimator is a combines a linear mixed effects regression model and a logistic
    mixed effects regression model via a two-stage modeling approach. The estimator's mean
    squared error is estimated via a parametric bootstrap method. Chandra and others
    (2012, <doi:10.1080/03610918.2011.598991>) introduce and describe this estimator and mean
    squared error estimator. White and others (2024+, <doi:10.48550/arXiv.2402.03263>) describe the 
    applicability of this estimator to estimation of forest attributes and further assess the
    estimator's properties.

Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.