saekernel: Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

Version:	0.1.1
Depends:	R (≥ 2.10)
Imports:	stats
Suggests:	knitr, rmarkdown, covr
Published:	2021-06-04
DOI:	10.32614/CRAN.package.saekernel
Author:	Wicak Surya Hasani[aut, cre], Azka Ubaidillah[aut]
Maintainer:	Wicak Surya Hasani <221710052 at stis.ac.id>
BugReports:	https://github.com/wicaksh/saekernel/issues
License:	GPL-3
URL:	https://github.com/wicaksh/saekernel
NeedsCompilation:	no
Materials:	README
CRAN checks:	saekernel results

Documentation:

Reference manual:	saekernel.pdf
Vignettes:	wicaksh_vignette

Downloads:

Package source:	saekernel_0.1.1.tar.gz
Windows binaries:	r-devel: saekernel_0.1.1.zip, r-release: saekernel_0.1.1.zip, r-oldrel: saekernel_0.1.1.zip
macOS binaries:	r-release (arm64): saekernel_0.1.1.tgz, r-oldrel (arm64): saekernel_0.1.1.tgz, r-release (x86_64): saekernel_0.1.1.tgz, r-oldrel (x86_64): saekernel_0.1.1.tgz

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