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 gmail.com> |
| License: | MIT + fileLICENSE |
| URL: | https://harvard-ufds.github.io/saeczi/ |
| NeedsCompilation: | yes |
| Materials: | README,NEWS |
| CRAN checks: | saeczi results |
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