2010:
Using R for Data Management, Statistical Analysis, and Graphics, by Nicholas Horton and Ken Kleinman
This is a new idea for an R book. Rather than introduce R, or explore some topic in detail, we explore a wide variety of applications and tasks, outlining the packages that can meet your needs and giving examples of many of them in practice, with full code and output examples included. This book is based on our SAS and R book, but includes new examples and additional materials, as well as an expanded introduction to R. Great for statistical analysts with no experience with R but with a need for a broader summary reference than can be found in other texts.
Using SAS for Data Management, Statistical Analysis, and Graphics, by Ken Kleinman and Nicholas Horton
This book is a concise reference for SAS, covering many models, graphical techniques, and data management tasks. It is a broad and shallow summary, providing a way into SAS's on-line documentation. For example, if you need to know how to do a probit regression, or transform your data from a wide data set into a tall one, you'll find the basics of how to do them here. Then you can look in the documentation for refinements.This book is based on our SAS and R book, but includes new examples and additional materials, as well as an expanded introduction to SAS. Great for statistical analysts with no experience with SAS but with a need for a broader summary reference than can be found in other texts.
2009:
SAS and R: Data Management, Statistical Analysis, and Graphics, by Ken Kleinman and Nicholas Horton
The notion behind this reference is that SAS and R are languages, and that once you know one language, you can use that knowledge to speed up your learning of the other. We explore data management issues such as getting your data into and out of SAS and R, data analysis issues that ought to be trivial but can be hard to find, such as fitting a linear regression model with no intercept, more complicated models, and graphical applications from scatterplots to scatterplot matrices. Extensive code examples with output are displayed, and code and data are available for download, so that you can easily replicate our results in the privacy of your own room.
2005:
Spatial and Syndromic Surveillance for Public Health, edited by Andrew Lawson and Ken Kleinman
A collection of papers surveying the statistical landscape of surveillance using spatial data for public health purposes, with contributions from some of the main players in this area.
