Package: saeczi 0.2.0.9000
Josh Yamamoto
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.
Authors:
saeczi_0.2.0.9000.tar.gz
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saeczi.pdf |saeczi.html✨
saeczi/json (API)
NEWS
| # Install saeczi in R: |
| install.packages('saeczi', repos = c('https://harvard-ufds.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/harvard-ufds/saeczi/issues
Last updated 20 days agofrom:11c22394fe6ea78e4c75a20790293598f32783ee
Exports:saeczi
Dependencies:bootbriocallrclicodetoolscrayondescdiffobjdigestdplyrevaluatefansifsfurrrfuturegenericsglobalsgluejsonlitelatticelifecyclelistenvlme4magrittrMASSMatrixminqanlmenloptrparallellypillarpkgbuildpkgconfigpkgloadpraiseprocessxprogressrpspurrrR6RcppRcppEigenrematch2rlangrprojroottestthattibbletidyselectutf8vctrswaldowithr
