RThe R project as a free software environment enables everyone to contribute to it. Thus, new methodology is often fast implemented in R. This is also the case for small area estimation methods. For beginners in R, the introduction to small area estimation techniques of the Asian Development Bank provides, among many online tutorials, an introduction to data management using R (Chapter III). There is a large number of packages that offer the estimation of small area estimates. A first overview is available on the CRAN TASK View for Official Statistics. Besides the packages for standard linear mixed models (nlme and lme4), it proposes six packages for small area estimation: sae, emdi, rsae, hbsae, JoSAE, BayesSAE. The figure below shows the number of downloads by months for these six packages to give an indication of their usage. For all packages, it can be seen that the number of downloads increased over time. Some packages provide a wide range of different methods, while others are more specialized on a specific method. The following table gives an overview of some R packages for SAE but it has no claim to completeness. The names of the packages serve as a link to the CRAN page while the model type columns gives a first indication which model types are available in the package. For applying the available functions, the vignettes and papers about the packages are useful. These usually contain examples and explanations on how to use the implemented functions. Overview of R packages for small area estimation Package | Model type | Vignettes/Paper |
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| Unit | Area |
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sae | X | X | | emdi | X | X | | Povmap - extension to the 'emdi' package (The R package 'povmap' supports small area estimation of means and poverty headcount rates. It adds several new features to the 'emdi' package (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). These include new options for incorporating survey weights, ex-post benchmarking of estimates, two additional transformations, several new convenient functions to assist with reporting results, and a wrapper function to facilitate access from 'Stata'.) | X | X | | rsae | X | X | | hbsae | X | X |
| JoSAE | X | X | | BayesSAE |
| X |
| saery |
| X |
| mme |
| X | | smallarea |
| X | | saeRobust |
| X | | msae |
| X |
| MIND | X |
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