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:
msae_0.1.5.tar.gz
msae_0.1.5.zip(r-4.7-any)msae_0.1.5.zip(r-4.6-any)
msae_0.1.5.tar.gz(r-4.7-any)msae_0.1.5.tar.gz(r-4.6-any)
msae_0.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
msae/json (API)
| # Install 'msae' in R: |
| install.packages('msae', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:418d7793ab. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 119 | ||
| source / vignettes | OK | 158 | ||
| linux-release | OK | 105 | ||
| windows-devel | OK | 69 | ||
| windows-release | OK | 65 | ||
| wasm-release | OK | 96 |
