c(person("Ann-Kristin", "Kreutzmann", role="aut", email="ann-kristin.kreutzmann@fu-berlin.de"),
person("Soeren", "Pannier", role=c("aut", "cre"), email="soeren.pannier@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("Matthias", "Templ", role="aut", email = "templ@statistik.tuwien.ac.at"),
person("Nikos", "Tzavidis", role="aut", email = "N.TZAVIDIS@soton.ac.uk"))
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
exported to excel.
nlme, FNN, moments, ggplot2, MuMIn, gridExtra, openxlsx,
ggmap, reshape2, graphics, stats, parallelMap, HLMdiag,
parallel, simFrame, boot, Hmisc, rgeos, maptools, MASS
Ann-Kristin Kreutzmann [aut],
Soeren Pannier [aut, cre],
Natalia Rojas-Perilla [aut],
Timo Schmid [aut],
Matthias Templ [aut],
Nikos Tzavidis [aut]

nlme, FNN, moments, ggplot2, MuMIn, gridExtra, openxlsx,
ggmap, reshape2, graphics, stats, parallelMap, HLMdiag,
parallel, simFrame, boot, rgeos, maptools, MASS

nlme, moments, ggplot2, MuMIn, gridExtra, openxlsx, reshape2,
graphics, stats, parallelMap, HLMdiag, parallel, boot, rgeos,
maptools, MASS, readODS

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
exported to excel. For a detailed description of the package and the methods used
see "The {R} Package {emdi} for Estimating and Mapping Regionally Disaggregated Indicators"
by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>.