Package: saeHB.spatial 0.1.1

Arina Mana Sikana

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:Arina Mana Sikana [aut, cre], Azka Ubaidillah [aut]

saeHB.spatial_0.1.1.tar.gz
saeHB.spatial_0.1.1.tar.gz(r-4.5-noble)saeHB.spatial_0.1.1.tar.gz(r-4.4-noble)
saeHB.spatial_0.1.1.tgz(r-4.4-emscripten)saeHB.spatial_0.1.1.tgz(r-4.3-emscripten)
saeHB.spatial.pdf |saeHB.spatial.html✨
saeHB.spatial/json (API)

# Install 'saeHB.spatial' in R:
install.packages('saeHB.spatial', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/arinams/saehb.spatial/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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

jagscpp

2.00 score 6 scripts 61 downloads 1 exports 11 dependencies

Last updated 3 days agofrom:64b97d214f. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linuxOKNov 25 2024

Exports:sar.normal

Dependencies:clicodagluelatticelifecyclemagrittrrjagsrlangstringistringrvctrs

saeHB_spatial

Rendered fromsaeHB_spatial.Rmdusingknitr::rmarkdownon Nov 25 2024.

Last update: 2024-11-22
Started: 2022-03-02