Package: saeczi
Type: Package
Title: Small Area Estimation for Continuous Zero Inflated Data
Version: 0.2.0.9000
Authors@R: c(person("Josh", "Yamamoto", role = c("aut", "cre"),
                    email = "joshuayamamoto5@gmail.com"),
             person("Dinan", "Elsyad", role = "aut"),
             person("Grayson", "White", role = "aut"),
             person("Julian", "Schmitt", role = "aut"),
             person("Niels", "Korsgaard", role = "aut"),
             person("Kelly", "McConville", role = "aut"),
             person("Kate", "Hu", role = "aut"))
Maintainer: Josh Yamamoto <joshuayamamoto5@gmail.com>
Description: Provides functionality to fit a zero-inflated estimator
        for small area estimation. This estimator is a combines a
        linear mixed effects regression model and a logistic mixed
        effects regression model via a two-stage modeling approach. The
        estimator's mean squared error is estimated via a parametric
        bootstrap method. Chandra and others (2012,
        <doi:10.1080/03610918.2011.598991>) introduce and describe this
        estimator and mean squared error estimator. White and others
        (2024+, <doi:10.48550/arXiv.2402.03263>) describe the
        applicability of this estimator to estimation of forest
        attributes and further assess the estimator's properties.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, lme4, purrr, progressr, furrr, future, rlang, Rcpp
RoxygenNote: 7.3.1
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Depends: R (>= 4.1.0)
LinkingTo: Rcpp, RcppEigen
URL: https://harvard-ufds.github.io/saeczi/
Config/pak/sysreqs: cmake make
Repository: https://harvard-ufds.r-universe.dev
Date/Publication: 2025-02-07 23:00:08 UTC
RemoteUrl: https://github.com/harvard-ufds/saeczi
RemoteRef: HEAD
RemoteSha: 47c4702be0cff3d6662946dbcbc1e5d4e05b917a
NeedsCompilation: yes
Packaged: 2026-10-14 09:57:04 UTC; root
Author: Josh Yamamoto [aut, cre],
  Dinan Elsyad [aut],
  Grayson White [aut],
  Julian Schmitt [aut],
  Niels Korsgaard [aut],
  Kelly McConville [aut],
  Kate Hu [aut]