Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.

Documentation

Manual: saeRobust.pdf
Vignette: fixedPoint

Maintainer: Sebastian Warnholz <Sebastian.Warnholz at fu-berlin.de>

Author(s): Sebastian Warnholz*

Install package and any missing dependencies by running this line in your R console:

install.packages("saeRobust")

Depends R (>= 3.3), methods, aoos
Imports assertthat, ggplot2, Matrix, magrittr, MASS, modules, memoise, Rcpp, spdep
Suggests knitr, rmarkdown, sae, saeSim, testthat
Enhances
Linking to Rcpp, RcppArmadillo
Reverse
depends
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imports
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suggests
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enhances
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linking to

Package saeRobust
Materials
URL
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Version 0.1.0
Published 2016-05-16
License MIT + file LICENSE
BugReports https://github.com/wahani/saeRobust/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks saeRobust check results
Package source saeRobust_0.1.0.tar.gz