FT: speed up the bootstrap functions

[ ] reuse the QR decomposition from lm in the regressions fitted in the residual bootstrap
[ ] check whether the speed gap between our comp_boot_emp function and covBS from the sandwich package is worth justifying the use of that package. Note that covBS does not return the coefficients estimates
[ ] sample rademacher weights with Rcpp in the multiplier bootstrap
[ ] sample the indices in empirical and multiplier bootstrap in the same way and try sampling the indices in a list
[x] in comp_boot_mul_wgt, change if statement to switch
[x] transform the dataframe to tibble only once in comp_boot_mul
[x] generate the datasets for the empirical bootstrap "on the fly" when calling fit_reg rather than storing them in a list by calling comp_boot_emp_samples

shamindras / maars