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.

Getting started

Package details

AuthorJosh Yamamoto [aut, cre], Dinan Elsyad [aut], Grayson White [aut], Julian Schmitt [aut], Niels Korsgaard [aut], Kelly McConville [aut], Kate Hu [aut]
MaintainerJosh Yamamoto <joshuayamamoto5@gmail.com>
LicenseMIT + file LICENSE
Version0.2.0
URL https://harvard-ufds.github.io/saeczi/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("saeczi")

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saeczi documentation built on June 22, 2024, 10:54 a.m.