Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.

Provides tools for small area estimation using a projection
    estimator with a linear multilevel working model. The main function fits
    a multilevel model to a smaller survey containing the response variable
    and auxiliary predictors, predicts outcomes in a larger projection survey,
    and computes domain-level estimates with a design-based residual
    correction.

Provides function for small area estimation at area level
    using averaging pseudo area level model for variables of interest.
    A dataset produced by data generation is also provided. This package
    estimates small areas at the village level and then aggregates them
    to the sub-district, region, and provincial levels.

Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.

Functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) <doi:10.2307/3315407>. The REML (Residual Maximum Likelihood) method is used for fitting the model.

Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.