Package: saebnocov 0.1.0
saebnocov: Small Area Estimation using Empirical Bayes without Auxiliary Variable
Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.
Authors:
saebnocov_0.1.0.tar.gz
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saebnocov_0.1.0.tgz(r-4.4-any)saebnocov_0.1.0.tgz(r-4.3-any)
saebnocov_0.1.0.tar.gz(r-4.5-noble)saebnocov_0.1.0.tar.gz(r-4.4-noble)
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saebnocov.pdf |saebnocov.html✨
saebnocov/json (API)
| # Install 'saebnocov' in R: |
| install.packages('saebnocov', repos = c('https://fika-fianda.r-universe.dev', 'https://cloud.r-project.org')) |
- dataEB - Sample Data for Practice
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:5048522747. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | OK | Nov 10 2024 |
| R-4.5-linux | OK | Nov 10 2024 |
| R-4.4-win | OK | Nov 10 2024 |
| R-4.4-mac | OK | Nov 10 2024 |
| R-4.3-win | OK | Nov 10 2024 |
| R-4.3-mac | OK | Nov 10 2024 |
Exports:alphabetaEBbootstrapEBEBnaiveestEBnaivejackknifeEBmatrixClairematrixRaomomentClairemomentRaonewtonRaphsonCnewtonRaphsonRpcapdirvectorClairevectorRao
Dependencies:clidescrdplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithrxtable
