Package: saeHB.spatial 0.1.1
saeHB.spatial: Small Area Estimation Hierarchical Bayes For Spatial Model
Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Authors:
saeHB.spatial_0.1.1.tar.gz
saeHB.spatial_0.1.1.zip(r-4.7)saeHB.spatial_0.1.1.zip(r-4.6)saeHB.spatial_0.1.1.zip(r-4.5)
saeHB.spatial_0.1.1.tgz(r-4.6-any)saeHB.spatial_0.1.1.tgz(r-4.5-any)
saeHB.spatial_0.1.1.tar.gz(r-4.6-any)saeHB.spatial_0.1.1.tar.gz(r-4.5-any)
saeHB.spatial_0.1.1.tgz(r-4.5-emscripten)
saeHB.spatial.pdf |saeHB.spatial.html✨
saeHB.spatial/json (API)
| # Install 'saeHB.spatial' in R: |
| install.packages('saeHB.spatial', repos = c('https://arinams.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/arinams/saehb.spatial/issues
- prox.mat - Proximity Matrix for Small Area Estimation under Spatial Simultaneous Autoregressive (SAR) Model
- sp.norm - Synthetic Data for Small Area Estimation under Spatial Simultaneous Autoregressive (SAR) Model and Normal Distribution
- sp.normNs - Synthetic Data for Small Area Estimation under Spatial Simultaneous Autoregressive (SAR) Model and Normal Distribution with non-sampled area
Last updated from:f72fbe3d79. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 115 | ||
| source / vignettes | OK | 161 | ||
| linux-release-x86_64 | OK | 116 | ||
| macos-release-arm64 | OK | 177 | ||
| macos-oldrel-arm64 | OK | 183 | ||
| windows-devel | OK | 92 | ||
| windows-release | OK | 68 | ||
| windows-oldrel | OK | 72 | ||
| wasm-release | OK | 93 |
Exports:sar.normal
Dependencies:clicodagluelatticelifecyclemagrittrrjagsrlangstringistringrvctrs
