saeczi: Small Area Estimation for Continuous Zero Inflated Data

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.

Version: 0.2.0
Depends: R (≥ 4.1.0)
Imports: dplyr, lme4, purrr, progressr, furrr, future, rlang, Rcpp
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0)
Published: 2024-06-06
DOI: 10.32614/CRAN.package.saeczi
Author: Josh Yamamoto [aut, cre], Dinan Elsyad [aut], Grayson White [aut], Julian Schmitt [aut], Niels Korsgaard [aut], Kelly McConville [aut], Kate Hu [aut]
Maintainer: Josh Yamamoto <joshuayamamoto5 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: saeczi results


Reference manual: saeczi.pdf


Package source: saeczi_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): saeczi_0.2.0.tgz, r-oldrel (arm64): saeczi_0.2.0.tgz, r-release (x86_64): saeczi_0.2.0.tgz, r-oldrel (x86_64): saeczi_0.2.0.tgz
Old sources: saeczi archive


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