Robust correlation in R? I would like to perform a robust correlation on a small sample (n<30). 
What is the best estimation method to use?
I tried to get an overview over the plenty methods for robust statistics provided in R - I would be happy if anyone could give me some recommendations
 A: MASS::cov.rob (link to man page) has two methods for robust covariances, which you can standardize to correlations with cov2cor.  @whuber is right that the "best" method will depend on what you want to do with it, though..
A: I implemented these correlation measures in R, it is super easy using robustbase package:
http://www.stat.tugraz.at/AJS/ausg111+2/111+2Shevlyakov.pdf
The assessment of performance for contaminated sample case is provided in the end of the article (for n=20 and n=1000). You may concentrate on $Q_n$ correlation, it works the best according to the assessment.
UPD: I recently found myself googling for a robust correlation code in R and found out this thread again. Here is the code:

robust_correlation <- function(robust_std, estimation_of_center_x, estimation_of_center_y, x, y) {
  square_root_of_two <- sqrt(2) 
  std_of_x <- robust_std(x)
  std_of_y <- robust_std(y)
  first_component = (x - estimation_of_center_x) / (square_root_of_two * std_of_x)
  second_component = (y - estimation_of_center_y) / (square_root_of_two * std_of_y)
  u = first_component + second_component
  v = first_component - second_component
  var_of_u = robust_std(u) ** 2
  var_of_v = robust_std(v) ** 2
  r = (var_of_u - var_of_v) / (var_of_u + var_of_v + 10**-10)
  return®
}
