The Strategic Goals
- improve Zelig's robustness and stability through the development of an extensive test suite
- improve Zelig's documentation
- increase adoption
- build a user community
- begin next stage research to implement advanced models
Cite Zelig, please reference:
Imai, Kosuke, Gary King, and Olivia Lau. 2008. “Toward A Common Framework for Statistical Analysis and Development.” Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913, http://j.mp/msE15c.
Choirat, Christine; Christopher Gandrud, James Honaker; Kosuke Imai; Gary King; Olivia Lau. 2017. Zelig: Everyone's Statistical Software, Version 5.0-15, URL: http://ZeligProject.org.
We also thank all contributors to the Zelig project, including: Vito D’Orazio, Jennifer McGrath, Muhammed Y. Idris, Ista Zhan, Justin Grimmer, Jason Wittenberg, Badri Narayan Bhaskar, Skyler J. Cranmer, Ben Goodrich, Ying Lu, Patrick Lam, Nicholas Carnes, Alexander D’Amour, Delia Bailey, Ferdinand Alimadhi, Elena Villalon, Matt Owen.
Zelig (noun) /ˈzɛlɪɡ/ : An entity with chameleon like characteristics, able to change appearance and form to fit appropriately in any circumstance.
Leonard Zelig was born in Brooklyn, New York at the turn of the century into a Jewish immigrant family. He gained notoriety and celebrity status during the 1920s due to his supernatural ability to look and act like whomever was around him. F. Scott Fitzgerald penned in his memoirs of meeting Leonard Zellman, a charming and impeccably dressed aristocrat, at an affluent garden party in Long Island. Lou Zelig turned up soon after at the New York Yankees spring training camp in Florida. Later, he was seen as a member of Al Capone’s Cosa Nostra and a black jazz musician in Chicago. Leonard inspired Woody Allen’s 1983 fictionalized documentary film, Zelig, about this “chameleon man” who changed his appearance and persona to comfortably blend into his surroundings and integrate himself into important historical events. Similarly, Zelig: Everyone's Statistical Software is intended to fit in every situation, and to work for every model and approach.