title: BCa confidence interval using pre-bootstrapped data
body: <p>I want to calculate a BCa confidence interval using the function <em>boot.ci</em> in the <em>boot</em> package. I do already have bootstrap replicates (<strong>not</strong> calculated using the <em>boot</em> function provided by the package). 
  To calculate a BCa confidence interval, it should be sufficient to pass the bootstrap replicates together with the observed statistic to the <em>boot.ci</em> function (parameters <em>t</em> and <em>t0</em>). However, technically, <em>boot.ci</em> requires an object of class "<em>boot</em>" to be the first argument in the function call. It seems that it is possible to pass just some arbitrary <em>boot</em> object to the function, as long as the number of replicates (<em>R</em>) is appropiate. However, calculating a faked boot object which is actually not needed seems a little bit inefficient. Another possibility is to construct a faked boot object from scratch, as discussed here (<a href="https://stackoverflow.com/questions/41786693/how-can-i-use-pre-bootstrapped-data-to-obtain-a-bca-confidence-interval">How can I use pre bootstrapped data to obtain a BCa confidence interval?</a>) and here (<a href="https://stackoverflow.com/questions/31314056/bootstrapping-confidence-intervals-in-r-bca-method-and-prescribed-resamples">Bootstrapping confidence intervals in R: BCa method and prescribed resamples</a>). This avoids at least wasting runtime, but is still not very elegant. I wonder if there is anybody who knows an even smarter solution (e.g. modified code for <em>boot.ci</em>). Thanks a lot!   </p>