Robert A. Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is also the creator of http://r4stats.com, a popular web site devoted to analyzing trends in analytics software and helping people learn the R language. Bob is an ASA Accredited Professional Statistician™ with 30 years of experience and is currently the manager of OIT Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. He has taught workshops on research computing topics for more than 500 organizations and currently offers training in partnership with DataCamp.com, Revolution Analytics (acquired by Microsoft), RStudio and Xerox Learning Services. Bob has written or coauthored over 70 articles published in scientific journals and conference proceedings, and has provided guidance on more than 1,000 graduate theses and dissertations.
Bob has served on the advisory boards of SAS Institute, SPSS Inc., StatAce OOD, the Statistical Graphics Corporation, and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, StatAce, JMP, STATGRAPHICS and numerous R packages. His research interests include statistical computing, data graphics and visualization, text analytics, and data mining.
Joseph M. Hilbe is Solar System Ambassador with NASA/Jet Propulsion Laboratory, California Institute of Technology, an adjunct professor of statistics at Arizona State, and emeritus professor at the University of Hawaii. He is an elected Fellow of the American Statistical Association and of the Royal Statistical Society and is an elected member of the International Statistical Institute.
Professor Hilbe was the first editor of the Stata Technical Bulletin, later to become the Stata Journal, (1991–1993). and was software reviews editor for The American Statistician from 1997-2009. Hilbe is also the author of a number of books, including Logistic Regression Models, Negative Binomial Regression, and with Robert A. Muenchen, R for Stata users, and with J. Hardin, Generalized Linear Models and Extensions, and Generalized Estimating Equations.