Code a function in R

I need a simple R function that implements a wild-t bootstrap for a given regression model, the wild-t bootstrap being a method to get appropriate standard errors in the presence of clustering. Note that I tagged C programming because R was not available, and most C programmers know R, I guess. My apologies if this is misleading. Having said that, if someone wants to carry this out in C with an R frontend, you are welcome. The function should take an lm-object and the name of a cluster variable as input (the cluster variable will be in the dataframe used that is stored in the lm-object. Would be cool if it was automatically retrieved from the dataframe stored in the lm-object), and computes wild-t boostrapped p-values for the regression coefficients. I will provide references for the method but it's simple: re-estimate leaving the regressor of interest out, take those residuals, multiply with 1 or -1, add to predicted dependend variable, re-estimate with regressor of interest back in, do this several times and compute p-value on t-stat with cluster-robust standard errors). The exact algorithm can be seen on slide 16 in the attached document. I will provide a more detailed paper later. There is stata code floating around in the world wide web for this procedure, and I would want to see a verification using this, but this can be discussed later. Moreover, it should be reasonably efficient, and I have reasonably large samples (no big data though), so it should work with large samples of around n=1million (and 100 to 150 regressors or so). An option over which variables to execute the bootstrap would be beneficial, as well. To sum up: I need an R function that takes an lm-object, the name of a cluster variable and a list (or vector) of the names (or indices) of regressors of interest as input, bootstraps wild-t cluster robust p-values for the regressors of interest, and gives back the lm-object with the corresponding p-values. The function should work with reasonably large datasets (n=1million). I am not an R programmer, but I am not an R newby either. I just don't have time at the moment, and I would appreciate help for this task. The steps involved strike me as quite simple, but I may overlook something. I can however provide a function that already carries out simple cluster-robust standard error estimation, so that it is easier to see what I have in mind. In any case, I'd suggest maybe around 40 USD for this.