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 + file LICENSE |
| URL: |
https://harvard-ufds.github.io/saeczi/ |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
saeczi results |
Documentation:
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