Fast Implementation of Small Area Estimation Methods R package fastsae version 0.1.0 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. Software Depends: R (>= 4.1.0) Imports: cli, ggplot2, lme4, methods, Rcpp, rlang, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: doParallel, dplyr, emdi, foreach, MASS, parallel, sae, knitr, rmarkdown, testthat (>= 3.0.0) Ridson Al Farizal P <alfrzlp@gmail.com> Comprehensive R Archive Network (CRAN) Ridson Al Farizal P [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-0617-0214>), Azka Ubaidillah [aut] (ORCID: <https://orcid.org/0000-0002-3597-0459>) GPL (>= 3) 2026-09-30 application/tgz https://CRAN.R-project.org/package=fastsae doi:10.32614/CRAN.package.fastsae