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add descriptive text to readme to provide more context about racially…
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karink520 authored Nov 22, 2021
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03397/status.svg)](https://doi.org/10.21105/joss.03397)

PyEI is a Python library for ecological inference.
PyEI is a Python library for ecological inference. The target audience is the analyst with an interest in the phenomenon called Racially Polarized Voting.

An important question in some voting rights and redistricting litigation in the U.S. is whether and to what degree voting is racially polarized.
In the setting of voting rights cases, the family of methods called "ecological inference" uses
observed data, pairing voting outcomes with demographic information
for each precinct in a given polity, to infer voting patterns for each demographic group.
Racially Polarized Voting is a legal concept developed through case law under the Voting Rights Act of 1965; its genesis is in the majority opinion of ***Thornburg v. Gingles (1982)***. Considered the “evidentiary linchpin” for vote dilution cases, RPV is a necessary, but not sufficient, condition that plaintiffs must satisfy for a valid claim.

Toward that end, ecological inference uses observed data (historical election results), pairing voting outcomes with demographic information
for each precinct in a given polity, to infer voting patterns for each demographic group.

PyEI brings together a variety of ecological inference methods in one place and facilitates reporting and plotting results; quantifying the uncertainty associated with results under a given model; making comparisons between methods; and bringing relevant diagnostic tools to bear on ecological inference methods.

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