For multiple imputation, I choose the R-package ‘mice‘ from van Buuren et al. You have to install it manually. There are also other packages that deal with it, see:

library.search("imputation")

If you are not familiar with the R-style to formulate linear models, start with

?lm
example(lm)

or read the usual intros and manuals that are linked via CRAN (or the contributed documentations) – otherwise search on the internet with your search machine of choice with the add-on ‘cran’ like ‘mulitple imputation cran’ or ‘missing data cran’, etc.

The folllowing are excerpts from the man-pages of ‘mice’-package commandos.

?read.table       # import of data - see tutorials and intros to R
                  # of 'how to import data'
library(help=mice)# what is inside package 'mice'?
library(mice)     # load library for MI
data(nhanes)      # use data from 'mice'-package
str(nhanes)
nhanes            # show data
?mice             # produce Multivariate Imputation by Chained Equations
imp <- mice(nhanes)
imp
str(imp)
?lm.mids          # Performs repeated linear regression
                  # on multiply imputed data set
lm.mids           # R-source code of 'lm.mids'
fit <- lm.mids(bmi~hyp+chl,data=imp)
fit
summary(fit)
str(fit)
?pool             #
pool(fit)         # pool results
summary(pool(fit))# better output
pool              # R-source code of 'pool'

That’s all. Multiple imputation (ordinary ANOVA) is quite easy to peform in R.