Memory efficient cluster bootstrap

Question

I have a very large dataset (10m observations but reduced to 14 essential variables), and am following the following thread: Cluster bootstrapped standard errors in R for plm functions

My codes after loading libraries are:

fe_pois <- fepois(totalcountdeals ~ logdist + inst_dist_std + whited_wu_std:findev_std | iso_o_code + sic2, vcov=~pair, data = cbma, nthreads = 2)

boot_feols <- boottest(fe_pois, clustid = "pair", param = "logdist", B = 199, nthreads = 2)

However, this fails with large enough memory. Any other solutions. I need to bootstrap standard errors because one of my regressors is an estimate.

I have also tried filtering the data, and run the above on a sub-sample, just for a try. New error there is;

Error in if (!is.numeric(lower) || !is.numeric(upper) || lower >= upper) stop("lower < upper  is not fulfilled") : 
  missing value where TRUE/FALSE needed