Time series area-level models for small area estimation. 
      The package supplements the functionality of the sae package. Specifically, it includes
      EBLUP fitting of the Rao-Yu model in the original form without a spatial component. 
      The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing
      the assumption of stationarity. Both univariate and multivariate applications are
      supported. Of particular note is the allowance for covariance of the area-level sample 
      estimates over time, as encountered in rotating panel designs such as the U.S. National 
      Crime Victimization Survey or present in a time-series of 5-year estimates from the 
      American Community Survey. Key references to the methods include
      J.N.K. Rao and I. Molina (2015, ISBN:9781118735787),
      J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and
      R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

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>.