emdi
Estimating and Mapping Disaggregated Indicators
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.
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Description file content
- Package
- emdi
- Title
- Estimating and Mapping Disaggregated Indicators
- Version
- 1.1.4
- Date
- 2018-10-10
- 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)
- Depends
- R (>= 3.3.0)
- License
- GPL-2
- URL
- LazyData
- true
- Copyright
- inst/COPYRIGHTS
- RoxygenNote
- 6.1.0
- Imports
- nlme, moments, ggplot2, MuMIn, gridExtra, openxlsx, reshape2, graphics, stats, parallelMap, HLMdiag, parallel, boot, rgeos, maptools, MASS, readODS
- Suggests
- testthat, R.rsp, simFrame, laeken
- VignetteBuilder
- R.rsp
- NeedsCompilation
- no
- Packaged
- 2018-10-10 16:26:50 UTC; spannier
- Author
- Ann-Kristin Kreutzmann [aut], Soeren Pannier [aut, cre], Natalia Rojas-Perilla [aut], Timo Schmid [aut], Matthias Templ [aut], Nikos Tzavidis [aut]
- Maintainer
- Soeren Pannier
- Repository
- CRAN
- Date/Publication
- 2018-10-10 17:10:07 UTC
