Package: ddi 0.1.0

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.

Authors:Shiro Kuriwaki [aut, cre]

ddi_0.1.0.tar.gz
ddi_0.1.0.zip(r-4.7-any)ddi_0.1.0.zip(r-4.6-any)ddi_0.1.0.zip(r-4.5-any)
ddi_0.1.0.tgz(r-4.6-any)ddi_0.1.0.tgz(r-4.5-any)
ddi_0.1.0.tar.gz(r-4.7-any)ddi_0.1.0.tar.gz(r-4.6-any)
ddi_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ddi/json (API)

# Install 'ddi' in R:
install.packages('ddi', repos = c('https://kuriwaki.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kuriwaki/ddi/issues

Datasets:
  • g2016 - 2016 General Election Results and Survey Estimates

On CRAN:

Conda:

3.18 score 3 stars 234 downloads 1 exports 0 dependencies

Last updated from:bbbc6772ef. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK107
source / vignettesOK144
linux-releaseOK88
macos-releaseOK151
macos-oldrelOK174
windows-develOK68
windows-releaseOK55
windows-oldrelOK60
wasm-releaseOK71

Exports:ddc

Dependencies: