Best resources on imputation in R This is my first question at stats. 
I need to impute some factors and numbers in my data set in R. What are my best options regarding packages and also a source to read more about the theory. 
 A: My Master's thesis revolved around the use of the mice package in R and I have only good things to say about it. I tried to use Amelia II but it just wasn't well suited to my data so I can't comment too much on that. The approach of the two packages does differ though so you may want to research which is better suited for your data. 
If you do end up taking the MICE route, here are some papers I would recommend to get you off to a running start:

Azur, M. J., Stuart, E. A., Frangakis, C., and Leaf, P. J. (2011).
  Multiple imputation by chained equations: what is it and how does it
  work? International Journal of Methods in Psychiatric Research,
  20(1):40–49.
Buuren, S. and Groothuis-Oudshoorn, K. (2011). mice: Multivariate
  imputation by chained equations in R. Journal of Statistical Software,
  45(3):1–66.

I also really liked the following textbook, 

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

A: I found Chapter 3 in Regression Modeling Strategies by Frank Harrell to be a good overview. It covers types of missing data, strategies for imputation, and discusses simplistic and advanced methods. The packages recommended in that chapter are MICE and aregImpute in R. 
