Fast Implementation of Small Area Estimation Methods
Overview
Provides high-performance implementations of Small Area Estimation (SAE) methods leveraging C++ ('Rcpp', 'RcppArmadillo') and 'OpenMP' multi-threading. Supports standard area-level Fay-Herriot models (Fay and Herriot, 1979 doi:10.1080/01621459.1979.10482505), Spatial Fay-Herriot models (Pratesi and Salvati, 2008 doi:10.1002/env.861), Spatio-Temporal Fay-Herriot models (Marhuenda et al., 2013 doi:10.1016/j.csda.2013.01.016), and unit-level Battese-Harter-Fuller models (Battese et al., 1988 doi:10.1080/01621459.1988.10478561). Features include empirical best linear unbiased prediction (EBLUP), analytical and bootstrap Mean Squared Error (MSE) estimation, automatic handling of unsampled domains, and modern S3 diagnostic methods.
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Health
- OK13 OKNow
- OK5 OK2026-10-01
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- Help pages
- 17 (1 internal)
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 80%
- Documented parameters
- 94%
- Return-value docs
- 80%
- Help pages citing references
- 24%
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Checks run against github.com/ridsonap/fastsae on 2026-10-18.
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- Claude Codefirst seen 2026-09-15Named in .Rbuildignore: CLAUDE.mdPull request it wrote: opened by a project memberCommits: at least 62 crediting it in the message
- Claude Opus 5.559 commits · Oct 2026
- Claude Opus 53 commits · Sep 2026
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Code & Tests
Datasets
People & History
- 0.1.0Latest2026-09-30 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-09-30
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 41 KB / 5 files
- Download size
- 807 KB
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