Bootstrap aggregating (bagging) is a meta-algorithm based on averaging the results of multiple bootstrap samples. Often the parametric bootstrap approximation is better than the empirical bootstrap approximation. The T-diagram is a notation used to explain these compiler bootstrap techniques. The jackknife is a linear approximation of the bootstrap. On initial startup, the client software must bootstrap and find at least one other node. Usually the jackknife is easier to apply to complex sampling schemes than the bootstrap. CSS frameworks include Foundation, Blueprint, Bootstrap, Cascade Framework and Materialize. The call to the stationary bootstrap needs to specify an appropriate mean interval length. This conformal field theory is under active investigation using the method of the conformal bootstrap. We now can create a histogram of bootstrap means. This sampling process is repeated many times as for other bootstrap methods. The prediction errors are estimated by bootstrap re-sampling. This is not to be confused with the "root.hints" file used to bootstrap a resolver. In the (simple) block bootstrap, the variable of interest is split into non-overlapping blocks. Gatling comes up with a recorder to bootstrap a simulation.