Computationally Inexpensive Imputation Techniques in R I have a large data-frame (155257 x 21 to be specific) with only a few missing values. Say, some 2.16% of the values need to be imputed. The values are floating point numbers.

I'd like to use a method that is much faster than it is accurate, because of the size of the data-set and the fact that I don't have much to lose in a speed-accuracy tradeoff.

Running missForest() takes several hours while Hmisc's impute() function gives unsatisfactory results.

What functions in R might be useful in such (or similar) case?
Take a look at the h20 package https://cran.r-project.org/web/packages/h2o/h2o.pdf. 

Everything is designed with parallelization in mind. I've had great success with many of their implementations, in R and Scala. 

If you have to do it in R and are going for pure speed I doubt you'll find something faster.