Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation,
the model-based unit-level approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010) \doi{10.1002/cjs.10051}),
the area-level model (see "Estimates of income for small places: An
application of James-Stein procedures to Census Data" by Fay and Herriot (1979)
\doi{10.1080/01621459.1979.10482505}) and various extensions of it (adjusted variance
estimation methods, log and arcsin transformation, spatial, robust and measurement
error models), 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> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in {R}".
nlme, moments, ggplot2, MuMIn, gridExtra, openxlsx, reshape2,
stats, parallelMap, HLMdiag, parallel, boot, MASS, readODS,
formula.tools, saeRobust, spdep
testthat, R.rsp, simFrame, laeken, rgeos, maptools, graphics

c(person("Sylvia", "Harmening", role="aut", email = "sylvia.harmening@fu-berlin.de"),
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("Felix", "Skarke", role=("aut"), email="f.skarke@fu-berlin.de"),
person("Natalia", "Rojas-Perilla", role="aut", email = "natalia.rojas@fu-berlin.de"),
person("Nicola", "Salvati", role="aut", email = "nicola.salvati@unipi.it"),
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"),
person("Nora", "Würz", role="aut", email = "nora.wuerz@fu-berlin.de"))
Functions that support estimating, assessing and mapping regional
disaggregated indicators. So far, estimation methods comprise direct estimation,
the model-based unit-level approach Empirical Best Prediction (see "Small area
estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>),
the area-level model (see "Estimates of income for small places: An
application of James-Stein procedures to Census Data" by Fay and Herriot (1979)
<doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance
estimation methods, log and arcsin transformation, spatial, robust and measurement
error models), 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> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in {R}".
nlme, moments, ggplot2, MuMIn, gridExtra, openxlsx, reshape2,
stats, stringr, parallelMap, HLMdiag, parallel, boot, MASS,
readODS, formula.tools, saeRobust, spdep
Sylvia Harmening [aut],
Ann-Kristin Kreutzmann [aut],
Soeren Pannier [aut, cre],
Felix Skarke [aut],
Natalia Rojas-Perilla [aut],
Nicola Salvati [aut],
Timo Schmid [aut],
Matthias Templ [aut],
Nikos Tzavidis [aut],
Nora Würz [aut]