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:Josh Yamamoto [aut, cre], Dinan Elsyad [aut], Grayson White [aut], Julian Schmitt [aut], Niels Korsgaard [aut], Kelly McConville [aut], Kate Hu [aut]

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NEWS

# Install 'saeczi' in R:
install.packages('saeczi', repos = c('https://harvard-ufds.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/harvard-ufds/saeczi/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • pop - FIA Population Level Auxiliary Data for Oregon
  • samp - FIA sample data for Oregon

On CRAN:

saezero-inflation

4.94 score 4 stars 3 scripts 494 downloads 1 exports 35 dependencies

Last updated 4 days agofrom:d477350c66. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-win-x86_64OKNov 19 2024
R-4.5-linux-x86_64OKNov 19 2024
R-4.4-win-x86_64OKNov 19 2024
R-4.4-mac-x86_64OKNov 19 2024
R-4.4-mac-aarch64OKNov 19 2024
R-4.3-win-x86_64OKNov 19 2024
R-4.3-mac-x86_64OKNov 19 2024
R-4.3-mac-aarch64OKNov 19 2024

Exports:saeczi

Dependencies:bootclicodetoolsdigestdplyrfansifurrrfuturegenericsglobalsgluelatticelifecyclelistenvlme4magrittrMASSMatrixminqanlmenloptrparallellypillarpkgconfigprogressrpurrrR6RcppRcppEigenrlangtibbletidyselectutf8vctrswithr