Package: fastsae
Type: Package
Title: High-Performance Frequentist and Bayesian Small Area Estimation
        in R
Version: 1.0.0
Authors@R: c(
      person("Ridson", "Al Farizal P", role = c("aut", "cre","cph"), email = "alfrzlp@gmail.com", comment = c(ORCID = "0000-0003-0617-0214")),
      person("Azka", "Ubaidillah", role = "aut", email = "azka@stis.ac.id", comment = c(ORCID = "0000-0002-3597-0459")) 
    )
Description: High-performance Small Area Estimation (SAE) in R,
        implementing frequentist EBLUP models via compiled C++ and
        Bayesian hierarchical models via 'INLA' (Integrated Nested
        Laplace Approximations) or, optionally, full MCMC with
        'NumPyro' (Phan et al., 2019) <doi:10.48550/arXiv.1912.11554>
        on 'JAX' (CPU or NVIDIA GPU). Frequentist models include the
        area-level Fay-Herriot model (Fay and Herriot, 1979
        <doi:10.1080/01621459.1979.10482505>), spatial Fay-Herriot
        (Pratesi and Salvati, 2008 <doi:10.1007/s10260-007-0061-9>),
        spatio-temporal Fay-Herriot (Marhuenda et al., 2013
        <doi:10.1016/j.csda.2012.09.002>), unit-level
        Battese-Harter-Fuller (Battese et al., 1988
        <doi:10.1080/01621459.1988.10478561>), and two-fold subarea
        models (Torabi and Rao, 2014 <doi:10.1016/j.jmva.2014.02.001>)
        for nested area-subarea hierarchies. Bayesian models use 'INLA'
        (Rue et al., 2009 <doi:10.1111/j.1467-9868.2008.00700.x>),
        supporting 6 likelihood families (Gaussian, Beta, Binomial,
        Poisson, Negative Binomial, Gamma), spatial structures (BYM2,
        Besag ICAR), temporal dynamics (RW1, RW2, AR1), and
        spatio-temporal interactions. Key features include analytical
        and bootstrap MSE estimation, benchmarking to aggregate targets
        (Rao and Molina, 2015 <doi:10.1002/9781118735855>), automatic
        handling of unsampled domains, and S3 methods for summaries,
        diagnostics and plots.
License: GPL (>= 3)
URL: https://ridsonap.github.io/fastsae/,
        https://github.com/ridsonap/fastsae
BugReports: https://github.com/ridsonap/fastsae/issues
Encoding: UTF-8
Language: en-US
LazyData: true
Additional_repositories: https://inla.r-inla-download.org/R/stable
Imports: cli, ggplot2, lifecycle, Matrix, methods, Rcpp, rlang, stats,
        utils
Suggests: covr, doParallel, dplyr, emdi, foreach, INLA, lme4, MASS,
        parallel, reticulate, sae, spdep, spam, sf, sn, tipsae, rstan,
        rstantools, writexl, openxlsx, knitr, rmarkdown, testthat (>=
        3.0.0)
VignetteBuilder: knitr
SystemRequirements: Python (>= 3.10) with 'jax' and 'numpyro'
        (optional, for hb_area(engine = "jax"); see
        setup_numpyro_env())
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Depends: R (>= 4.1.0)
Collate: utils.R inla_utils.R RcppExports.R data.R methods.R autoplot.R
        gvf.R create_weights.R eblup_fh.R eblup_sfh.R eblup_stfh.R
        eblup_twofold.R eblup_bhf.R engine.R hb_area.R hb_area_jax.R
        python_env.R hb_unit.R hb_twofold.R sim_spatial_weights.R
        sim_area_data.R sim_series_data.R diagnose.R spatial_test.R
        benchmark.R compare_sae.R export_sae.R map_sae.R
Config/roxygen2/version: 8.0.0
Config/testthat/edition: 3
Config/pak/sysreqs: python3
Repository: https://ridsonap.r-universe.dev
Date/Publication: 2026-10-17 04:12:40 UTC
RemoteUrl: https://github.com/ridsonap/fastsae
RemoteRef: HEAD
RemoteSha: bc711c7b900272b8bd9ff17e38a132730a6c5f71
NeedsCompilation: yes
Packaged: 2026-10-17 04:51:18 UTC; root
Author: 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>)
Maintainer: Ridson Al Farizal P <alfrzlp@gmail.com>