Imports: nlme, moments, ggplot2, MuMIn, gridExtra, openxlsx, reshape2,
graphics, stats, parallelMap, HLMdiag, parallel, boot, rgeos,
Suggests: testthat, R.rsp, simFrame, laeken

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"

Authors@R: c(person("Sylvia", "Harmening", role="aut", email = "sylvia.harmening@fu-berlin.de"),
person("Nicola", "Salvati", role="aut", email = "nicola.salvati@unipi.it"),
person("Nora", "Würz", role="aut", email = "nora.wuerz@fu-berlin.de"))
disaggregated indicators. So far, estimation methods comprise direct estimation,
the model-based unit-level approach Empirical Best Prediction (see "Small area
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),
maptools, MASS, readODS, formula.tools, saeRobust, spdep

estimation of poverty indicators" by Molina and Rao (2010) \doi{10.1002/cjs.10051}),
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
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}".
stats, parallelMap, HLMdiag, parallel, boot, MASS, readODS,
Suggests: testthat, R.rsp, simFrame, laeken, rgeos, maptools, graphics

<doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance
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}".
stats, stringr, parallelMap, HLMdiag, parallel, boot, MASS,