Title: Estimating and Mapping Disaggregated Indicators
Authors@R: c(person("Ann-Kristin", "Kreutzmann", role="aut", email="ann-kristin.kreutzmann@fu-berlin.de"),
person("Natalia", "Rojas-Perilla", role="aut", email = "natalia.rojas@fu-berlin.de"),
person("Timo", "Schmid", role="aut", email = "timo.schmid@fu-berlin.de"),
person("Nikos", "Tzavidis", role="aut", email = "N.TZAVIDIS@soton.ac.uk"),
person("Matthias", "Templ", role="aut", email = "templ@statistik.tuwien.ac.at"))
Description: Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise the model-based approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010)<doi:10.1002/cjs.10051>), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel.
Imports: nlme, FNN, moments, ggplot2, MuMIn, gridExtra, openxlsx,
ggmap, reshape2, graphics, stats, parallelMap, HLMdiag,

person("Soeren", "Pannier", role=c("aut", "cre"), email="soeren.pannier@fu-berlin.de"),
person("Matthias", "Templ", role="aut", email = "templ@statistik.tuwien.ac.at"),
person("Nikos", "Tzavidis", role="aut", email = "N.TZAVIDIS@soton.ac.uk"))
Description: Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation
and the model-based approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>),
as well as their precision estimates. The assessment of the used model
is supported by a summary and diagnostic plots. For a suitable presentation of
estimates, map plots can be easily created. Furthermore, results can easily be
parallel, simFrame, boot, Hmisc, rgeos, maptools, MASS

parallel, simFrame, boot, rgeos, maptools, MASS