There are a few rule-based models available in R. They differ in how they work (and I assume you want to use them for classification and not regression).
I would start with Quinlan's book . He goes into a lot of detail about how to make rules out of decision trees. Also look at Witten et al  to see how a lot of Quinlan's ideas have been extended (and these are implemented in
Then there is
C5.0 which has tree- and rule-based versions, including boosting methods. Quinlan hasn't published much in a while but he did open-source his code recently. There are some subtle differences between what he describes in his book and what is currently in
C5.0. The main changes are boosting and winnowing (AFAICT).
I just sent a book to Springer a few days ago that has a lot of detail on how this works, so hopefully that will be available soon if you don't want to sift thru a ton of C code.
There is an R package
C50 om CRAN that we made from his GPL version of
C5.0. There is an argument for
rules (to get a rule-based model instead of a tree) and
trials for boosting.
 Quinlan, R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
 Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (3rd ed.). Morgan Kaufmann.