The R package emdi for estimating and mapping regionally disaggregated indicators

Ann Kristin Kreutzmann, Sören Pannier, Natalia Rojas-Perilla, Timo Schmid, Matthias Templ, Nikos Tzavidis

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the head-count ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of data-driven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria.

Original languageEnglish
Article number7
JournalJournal of Statistical Software
Publication statusPublished - 2019
Externally publishedYes


  • Official statistics
  • Parallel computing
  • Small area estimation
  • Survey statistics
  • Visualization

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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