Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.

Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

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>.