... args to haldensify() to allow arbitrary arguments to be passed directly to fit_hal().use_future argument to haldensify(), instead reducing to calling future_mapply(), with sequential evaluation via plan(sequential).rsample.plot() method to more easily visualize how empirical risk changes across the sequence of explored regularization parameter values.ipw_shift() function for constructing IPW estimators of the mean counterfactual outcome of a stochastic shift intervention via haldensify.haldensify() to allow normalization of the density estimates to improve estimation stability. Note that this normalized density is actually g(A|W)/g(A), instead of the currently estimated g(A|W).