Package: saeHB 0.2.2

Zaza Yuda Perwira

saeHB: Small Area Estimation using Hierarchical Bayesian Method

Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Authors:Zaza Yuda Perwira [aut, cre], Azka Ubaidillah [aut], Ika Yuni Wulansari [aut]

saeHB_0.2.2.tar.gz
saeHB_0.2.2.tar.gz(r-4.5-noble)saeHB_0.2.2.tar.gz(r-4.4-noble)
saeHB_0.2.2.tgz(r-4.4-emscripten)saeHB_0.2.2.tgz(r-4.3-emscripten)
saeHB.pdf |saeHB.html✨
saeHB/json (API)

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

Peer review:

Bug tracker:https://github.com/zazaperwira/saehb/issues

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

jagscpp

2.70 score 3 scripts 230 downloads 14 exports 32 dependencies

Last updated 1 years agofrom:e8c0f38272. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linuxNOTEDec 02 2024

Exports:BetaBinomialExponentialExponentialDoubleGammaLogisticLognormalNegativeBinomialNormalPoissonPoissonGammaStudent_tStudent_tncWeibull

Dependencies:CARBayesdataclassclassIntclicodacpp11DBIe1071glueigraphKernSmoothlatticelifecyclemagrittrMASSMatrixnimblenumDerivpkgconfigpracmaproxyR6Rcpprjagsrlangs2sfstringistringrunitsvctrswk

saeHB_vignette

Rendered fromsaeHB_vignette.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2021-11-02
Started: 2021-11-02