msae: Multivariate Fay Herriot Models for Small Area Estimation

Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

Version:	0.1.5
Depends:	R (≥ 2.10)
Imports:	magic
Published:	2022-04-24
DOI:	10.32614/CRAN.package.msae
Author:	Novia Permatasari, Azka Ubaidillah
Maintainer:	Novia Permatasari <novia.permatasari at bps.go.id>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	msae results

Documentation:

Reference manual:

msae.pdf

Downloads:

Package source:	msae_0.1.5.tar.gz
Windows binaries:	r-devel: msae_0.1.5.zip, r-release: msae_0.1.5.zip, r-oldrel: msae_0.1.5.zip
macOS binaries:	r-release (arm64): msae_0.1.5.tgz, r-oldrel (arm64): msae_0.1.5.tgz, r-release (x86_64): msae_0.1.5.tgz, r-oldrel (x86_64): msae_0.1.5.tgz
Old sources:	msae archive

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