Package: msae 0.1.5

Novia Permatasari

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>.

Authors:Novia Permatasari, Azka Ubaidillah

msae_0.1.5.tar.gz
msae_0.1.5.tar.gz(r-4.5-noble)msae_0.1.5.tar.gz(r-4.4-noble)
msae_0.1.5.tgz(r-4.4-emscripten)msae_0.1.5.tgz(r-4.3-emscripten)
msae.pdf |msae.html✨
msae/json (API)

# Install 'msae' in R:
install.packages('msae', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • datasae1 - Data generated based on Multivariate Fay Herriot Model
  • datasae2 - Data generated based on Autoregressive Multivariate Fay Herriot Model
  • datasae3 - Data generated based on Heteroscedastic Autoregressive Multivariate Fay Herriot Model

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 265 downloads 4 exports 2 dependencies

Last updated 3 years agofrom:418d7793ab. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linuxOKMar 23 2025
R-4.4-linuxOKMar 23 2025

Exports:eblupMFH1eblupMFH2eblupMFH3eblupUFH

Dependencies:abindmagic