- Learning R
- Computational Statistics
- Graphics & Visualzations
- Complex Surveys
- Missing Data
- Propensity Score Analysis
- Multilevel Modeling
- Spatial / Geographic Information System (GIS)
- LaTeX
Kabacoff, R.J. (2011). R in Action: Data Analysis and Graphics with R. Shelter Island, NY: Manning.
Matloff, N. (2011). The Art of R Programming. San Francisco, CA: No Stratch Press
Dalgaard, P. (2008). Introductory Statistics with R (2nd Ed.). New York, NY: Springer.
Braun, W.J., & Murdoch, D.J. (2007). A First Course in Statistical Programming with R. Cambridge, UK: Cambridge University Press.
Chambers, J.M. (2008). Software for Data Analysis: Programming with R. New York: Springer.
Gentle, J. E. (2009). Computational Statistics. New York, NY: Springer.
Knuth, D. E. (1992). Literate Programming. California: Stanford University Center for the Study of Language and Information.
Rizzo, M. L. (2008). Statistical Computing with R. Boca Raton, FL: Taylor & Francis Group, LLC.
Cleveland, W. S. (1993). Visualizing Data. Summit, NJ: Hobart Press.
Tufte, E. R. (2001). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press LLC.
Unwin, A., Theus, M., & Hofmann, H. (2006). Graphics of Large Datasets: Visualizing a Million. New York, NY: Springer.
Wilkinson, L. (2005). The Grammar of Graphics (2nd Ed). New York, NY: Springer.
Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer.
Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Indianapolis, IN: Wiley Publishing, Inc.
Andrew Gelman and Anthony Unwin, Infovis and Statistical Graphics: Different Goals, Different Looks
Responses:
- Stephen Few, Are Infovis and Statistical Graphics Really All That Different?
- Robert Kosara, InfoVis Is So Much More: A Comment on Gelman and Unwin and an Invitation to Consider the Opportunities
- Paul Murrell, Comment
- Hadley Wickham, Graphical criticism: some historical notes
Rejoinder
Lumley, T. (2004). Analysis of complex surveys samples. Journal of Statistical Software, 9(8).
Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R. Hoboken, NJ: John Wiley & Sons, Inc.
Blackwell, M., Honaker, J., & King, G. (2010). Multiple overimputation: A unified approach to measurement error and missing data. Working Paper.
van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca Raton, FL: CRC Press.
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3).
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001). Analyzing incomplete political science data: An alternative algorithm for multiple imputation. American Political Science Review, 95(1), 49-69.
Rubin, D.B. (1976) Inference and missing data. Biometrika, 63, 581-592.
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. J. Wiley & Sons, New York.
Rubin, D.B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91(434).
Rosenbaum, P.R., & Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Rosenbaum, P.R. (2010). Design of Observational Studies. New York: Springer.
Pinheiro, J., & Bates, D. (2009). Mixed-Effects Models in S and S-PLUS. New York, NY: Springer.
Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge, MA: Cambridge University Press.
Bivand, R.S., Pebesma, E.J., & Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R. New York: Springer.
O'Sullivan, D., & Unwin, D.J. (2010). Geographic Information Analysis (2nd Ed.). Hoboken, NJ: John Wiley & Sons, Inc.
Beitzel, B.D. (2012). Formatting LaTeX documents in APA style (6th Edition) using the apa6 class. The PracTeX Journal, 1.
Grätzer, G. (2007). More Math Into LaTeX (4th Ed.). New York: Springer.
Shang, H.L. (2012). Writing posters with Beamerposter package in LaTeX. The PracTeX Journal, 1.