Title: Develop Treatment Rules with Observational Data
person("Noah", "Simon", email = "nrsimon@uw.edu", role="aut"))
Description: Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.

Title: Data sets from Devore's "Prob and Stat for Eng (5th ed)"
modifications by Douglas Bates <bates@stat.wisc.edu>
Description: Data sets and sample analyses from Jay L. Devore
(2000), "Probability and Statistics for Engineering and the

Author: Original by Jay L. Devore, modifications by Douglas Bates
Description: Data sets and sample analyses from Jay L. Devore (2000),
"Probability and Statistics for Engineering and the Sciences

Title: Data sets from Devore's "Prob and Stat for Eng (6th ed)"
(2003), "Probability and Statistics for Engineering and the

Packaged: Tue Sep  7 10:25:48 2004; bates

Description: Data sets and sample analyses from Jay L. Devore (2003),
Packaged: Wed Feb 8 09:37:15 2006; bates

Title: Data sets from Devore's "Prob and Stat for Eng (7th ed)"
<bates@stat.wisc.edu>, modifications  for 7th edition by John Verzani.
Description: Data sets and sample analyses from Jay L. Devore (2008),
Packaged: Wed Mar  7 17:31:05 2007; verzani

<bates@stat.wisc.edu>, modifications for 7th edition by John

Description: Decision forest: combining the predictions of
multiple independent decision tree models.