Introduction | The Fay-Herriot Model | Step-by-Step Example | 1. Load Package and Dataset | 2. Fit Fay-Herriot Model (eblup_fh) | 3. Model Summary and Coefficients | 4. Diagnostic Plots (autoplot) | Direct Estimates vs EBLUP | Mean Squared Error (MSE) Across Domains | Exact Numerical Equivalence with sae | References
This package is considered a duplicate. The official version of this package is found at:https://ridsonap.r-universe.dev/fastsae
Package: fastsae 0.1.0
Ridson Al Farizal P
fastsae: Fast Implementation of Small Area Estimation Methods
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.
Authors:
fastsae_0.1.0.tar.gz
fastsae_0.1.0.zip(r-4.7-x86_64)fastsae_0.1.0.zip(r-4.7-arm64)fastsae_0.1.0.zip(r-4.6-x86_64)fastsae_0.1.0.zip(r-4.6-arm64)
fastsae_0.1.0.tar.gz(r-4.7-arm64)fastsae_0.1.0.tar.gz(r-4.7-x86_64)fastsae_0.1.0.tar.gz(r-4.6-arm64)fastsae_0.1.0.tar.gz(r-4.6-x86_64)
fastsae_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
fastsae/json (API)
| # Install 'fastsae' in R: |
| install.packages('fastsae', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ridsonap/fastsae/issues
Pkgdown/docs site:https://ridsonap.github.io
Uses libs:
Datasets:
- cornsoybean - Corn and Soybean Survey and Satellite Data in 12 Iowa Counties
- cornsoybeanmeans - Corn and Soybean Mean Number of Pixels per Segment for 12 Iowa Counties
- mys - Mys: mean years of schooling people with disabilities.
- mys_panel - Mys_panel: mean years of schooling people with disabilities 2016 - 2026.
- mys_proxmat - Example proximity matrix
Last updated from:ff38bc1faf. Checks:7 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 209 | ||
| linux-devel-x86_64 | OK | 231 | ||
| source / vignettes | OK | 328 | ||
| windows-devel-x86_64 | OK | 281 | ||
| windows-release-arm64 | OK | 327 | ||
| windows-release-x86_64 | OK | 246 | ||
| wasm-release | OK | 184 |
Exports:autoploteblup_bhfeblup_fheblup_sfheblup_stfh
Dependencies:bootclicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelme4MASSMatrixminqanlmenloptrR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesvctrsviridisLitewithr
Getting Started with fastsae
Last update: 2026-09-30
Started: 2026-09-30
Performance & Scalability Benchmarks
Executive Summary | Benchmark Results Table | Interactive Benchmark Explorer | Architectural Insights: Why is fastsae so Fast?
Last update: 2026-09-30
Started: 2026-09-30
Spatial and Spatio-Temporal Models
Overview | 1. Spatial Fay-Herriot Model (eblup_sfh) | Model Formulation | Fitting the Model | Unsampled Domains and Spatial Kriging | Multi-Threaded Bootstrap MSE | 2. Spatio-Temporal Fay-Herriot Model (eblup_stfh) | Fitting the Spatio-Temporal Model | 3. Comparing Models with autoplot | References
Last update: 2026-09-30
Started: 2026-09-30
Unit-Level Estimation with Battese-Harter-Fuller
Overview | Model Formulation | Step-by-Step Example | 1. Data Preparation | 2. Fit BHF Model with Bootstrap MSE | 3. Inspect Domain Estimates | 4. Visualizing Results with autoplot | References
Last update: 2026-09-30
Started: 2026-09-30
