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 original Rao-Yu model, 
      which in the original form did not have 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>.

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.