c(person(given = "Reyhan",family = "Saadi",role = c("aut","cre"),email = "221810557@stis.ac.id"), person(given = "Azka",family = "Ubaidillah",role = "aut"))
Reyhan Saadi [aut, cre],
Azka Ubaidillah [aut]

Small Area Estimation using Hierarchical Bayesian under Zero
Inflated Negative Binomial Distribution
We designed this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Zero Inflated Negative Binomial Distribution. This package provides model using Univariate Zero Inflated 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 HB estimators which include the mean and the variation of mean, and the quantile. For the reference, see Rao,J.N.K & Molina (2015) <doi:10.1002/9781118735855>.

Small Area Estimation with Measurement Error
Muhammad Rifqi Mubarak [aut, cre],
Azka Ubaidillah [aut]
A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.

Compute MSPE Estimates for the Fay Herriot Model and Nested
Error Regression Model
c(person("Peiwen", "Xiao", role = c("aut", "cre"),
email = "2569613200@qq.com"),
person("Xiaohui", "Liu", role = "aut"),
person("Yuzi", "Liu", role = "aut"),
person("Shaochu", "Liu", role = "aut"),
person("Jiming", "Jiang", role = "ths"))
We describe a new R package entitled 'saeMSPE' for the well-known Fay Herriot model and nested error regression model in small area estimation. Based on this package, it is possible to easily compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for these two models.
Peiwen Xiao [aut, cre],
Xiaohui Liu [aut],
Yuzi Liu [aut],
Shaochu Liu [aut],
Jiming Jiang [ths]