ddi: The Data Defect Index for Samples that May not be IID

Implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results.

Version: 0.1.0
Depends: R (≥ 2.10)
Suggests: testthat (≥ 2.1.0), dplyr, tibble
Published: 2020-01-26
Author: Shiro Kuriwaki ORCID iD [aut, cre]
Maintainer: Shiro Kuriwaki <shirokuriwaki at gmail.com>
BugReports: http://github.com/kuriwaki/ddi/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/kuriwaki/ddi
NeedsCompilation: no
Materials: README NEWS
CRAN checks: ddi results


Reference manual: ddi.pdf


Package source: ddi_0.1.0.tar.gz
Windows binaries: r-devel: ddi_0.1.0.zip, r-release: ddi_0.1.0.zip, r-oldrel: ddi_0.1.0.zip
macOS binaries: r-release (arm64): ddi_0.1.0.tgz, r-release (x86_64): ddi_0.1.0.tgz, r-oldrel: ddi_0.1.0.tgz


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