saebnocov: Small Area Estimation using Empirical Bayes without Auxiliary Variable

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>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	descr, dplyr, rlang, stats
Suggests:	knitr, rmarkdown
Published:	2022-09-05
DOI:	10.32614/CRAN.package.saebnocov
Author:	Siti Rafika Fiandasari [aut, cre], Margaretha Ari Anggorowati [aut], Bahrul Ilmi Nasution [aut]
Maintainer:	Siti Rafika Fiandasari <fikafianda at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	saebnocov results

Documentation:

Reference manual:	saebnocov.pdf
Vignettes:	Best_Vignete_ever Best_Vignetee_ever

Downloads:

Package source:	saebnocov_0.1.0.tar.gz
Windows binaries:	r-devel: saebnocov_0.1.0.zip, r-release: saebnocov_0.1.0.zip, r-oldrel: saebnocov_0.1.0.zip
macOS binaries:	r-release (arm64): saebnocov_0.1.0.tgz, r-oldrel (arm64): saebnocov_0.1.0.tgz, r-release (x86_64): saebnocov_0.1.0.tgz, r-oldrel (x86_64): saebnocov_0.1.0.tgz

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