Small Area Estimation under Hurdle Negative Binomial
Distribution using Hierarchical Bayesian Method
Raka Ikmana [aut, cre], Azka Ubaidillah [aut]
We design this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Hurdle Negative Binomial Distribution. This package provides model using Univariate Hurdle Negative Binomial Distribution for variable of interest. This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the Hierarchical Bayes estimators which include the mean and the variation of mean. For references, see Hilbe (2011) <doi:10.1017/CBO9780511973420> and Rao (2015) <doi:10.1002/9781118735855>.

Small Area Estimation using Hierarchical Bayesian Method for Rao
Yu Model
Velia Tri Marliana [aut, cre], Azka Ubaidillah [aut]
We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).

Small Area Estimation under Spatial SAR Model using Hierarchical
Bayesian Method
Azka Ubaidillah [aut], Arina Mana Sikana [aut, cre]
Provides several functions and datasets for area level of Small Area Estimation under Spatial SAR Model using Hierarchical Bayesian (HB) Method.

Hierarchical Bayes Twofold Subarea Level Model SAE
c(person(given = "Azka",family = "Ubaidillah",role = "aut"),person(given = "Reyhan",family = "Saadi",role = c("aut","cre"),email = "221810557@stis.ac.id"))
We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
rjags, coda, stringr, stats, grDevices, graphics, data.table,
utils
Azka Ubaidillah [aut],
Reyhan Saadi [aut, cre]