boot_default: Bootstrap (default) Bootstrap function for the non-parametric...
In optinterv: Optimal Intervention

Bootstrap (default) Bootstrap function for the non-parametric and the nearest neighbor methods

1 2	boot_default(func, Y, Y_pos, X, X_std, control, wgt, n.quant, lambda, sigma, grp.size, n.boot, quick)

`func`	a function for weights calculation (nn / non_parm).
`Y`	the original outcome.
`Y_pos`	outcome after exponential transformation (if needed).
`X`	the original X matrix.
`X_std`	X matrix after standardization.
`control`	numeric data frame or matrix of factors to control for. these are factors that we can't consider while looking for the optimal intervention (e.g. race).
`wgt`	an optional vector of weights.
`n.quant`	number of quantiles to use when calculating CDF distance.
`lambda`	the lagrange multiplier. also known as the shadow price of an intervention.
`sigma`	distance penalty for the nearest-neighbors method.
`grp.size`	for the nearest-neighbors method; if the number of examples in each control group is smaller than grp.size, performs weight adjustment using `wgt_adjust`. else, calculate weights seperatly for each control group.
`n.boot`	number of bootstrap replications to use for the standard errors / confidence intervals calculation.
`quick`	logical. if TRUE, returns only E(X \| I=1) - E(X \| I=0) as an estimate. this estimate is used by `optint_by_group`.