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msae
View on CRAN: Click
here
Download and install msae package within the R console
Install from CRAN:
install.packages("msae")
Install from Github:
library("remotes")
install_github("cran/msae")
Install by package version:
library("remotes")
install_version("msae", "0.1.5")
Attach the package and use:
library("msae")
Maintained by
Novia Permatasari
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-20
Latest Update: 2022-04-24
Description:
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) .
How to cite:
Novia Permatasari (2020). msae: Multivariate Fay Herriot Models for Small Area Estimation. R package version 0.1.5, https://cran.r-project.org/web/packages/msae. Accessed 28 Jan. 2025.
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Complete documentation for msae
Functions, R codes and Examples using
the msae R package
Some associated functions: datasae1 . datasae2 . datasae3 . df2matR . eblupMFH1 . eblupMFH2 . eblupMFH3 . eblupUFH . msae .
Some associated R codes: data1.R . data2.R . data3.R . df2matR.R . eblupMFH1.R . eblupMFH2.R . eblupMFH3.R . eblupUFH.R . msae-package.R . Full msae package functions and examples
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