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.
- Version0.2.0
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?Yes
- Last release06/06/2024
Documentation
Team
Josh Yamamoto
MaintainerShow author detailsGrayson White
Kelly McConville
Show author detailsRolesAuthorDinan Elsyad
Show author detailsRolesAuthorJulian Schmitt
Show author detailsRolesAuthorNiels Korsgaard
Show author detailsRolesAuthorKate Hu
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 421 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 5,512 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Feb 20, 2025 with 63 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports8 packages
- Suggests1 package
- Linking To2 packages