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type: software
license: MIT
title: 'saeczi: Small Area Estimation for Continuous Zero Inflated Data'
version: 0.2.0.9000
abstract: 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, <https://doi.org/10.1080/03610918.2011.598991>) introduce and describe this
  estimator and mean squared error estimator. White and others (2024+, <https://doi.org/10.48550/arXiv.2402.03263>)
  describe the applicability of this estimator to estimation of forest attributes
  and further assess the estimator's properties.
authors:
- family-names: Yamamoto
  given-names: Josh
  email: joshuayamamoto5@gmail.com
- family-names: Elsyad
  given-names: Dinan
- family-names: White
  given-names: Grayson
- family-names: Schmitt
  given-names: Julian
- family-names: Korsgaard
  given-names: Niels
- family-names: McConville
  given-names: Kelly
- family-names: Hu
  given-names: Kate
repository: https://harvard-ufds.r-universe.dev
url: https://harvard-ufds.github.io/saeczi/
contact:
- family-names: Yamamoto
  given-names: Josh
  email: joshuayamamoto5@gmail.com