MC_sums                 Compute sums of a Monte Carlo vector for use in
                        resmoothing.
basis_poly              Estimate the score function of the d'th
                        covariate using a polynomial basis.
compare                 Generate simulation data and evaluate
                        estimators, with sample splitting.
compare_evaluate        Evaluate estimators by training nuisance
                        functions on training set and evaluating them
                        on test set.
compare_lm              Generate simulation data and evaluate OLS
                        estimator.
compare_partially_linear
                        Generate simulation data and evaluate partially
                        linear estimator.
compare_rothenhausler   Generate simulation data and evaluate
                        Rothenhausler estimator.
cv_resmooth             K-fold cross-validation for resmoothing
                        bandwidth.
cv_spline_score         K-fold cross-validation for spline_score.
drape                   Estimate the doubly-robust average partial
                        effect estimate of X on Y, in the presence of
                        Z.
fit_lasso_poly          Fit a lasso regression using quadratic
                        polynomial basis, with interactions.
fit_xgboost             Fit pre-tuned XGBoost regression for use in
                        simulations.
mixture_score           Population score function for the symmetric
                        mixture two Gaussian random variables.
new_X                   Generate a matrix of covariates for use in
                        resmoothing, in which the d'th column of X is
                        translated successively by the Kronecker
                        product of bw and MC_variates.
ng_pseudo_response      Generate pseudo responses as in Ng 1994 to
                        enable univariate score estimation by standard
                        smoothing spline regression.
partially_linear        Fit a doubly-robust partially linear regression
                        using the DoubleML package and pre-tuned
                        XGBoost regressions, for use in simulations.
resmooth                Resmooth the predictions of a fitted model
rmixture                Symmetric mixture two Gaussian random
                        variables.
rothenhausler_basis     Generate the modified quadratic basis of
                        Rothenhausler and Yu.
rothenhausler_yu        Estimate the average partial effect of using
                        the debiased lasso method of Rothenhausler and
                        Yu, using pre-tuned lasso penalties, for use in
                        simulations.
simulate_data           Generate simulation data.
sort_bin                Sort and bin x within a specified tolerance,
                        using hist().
spline_score            Univariate score estimation via the smoothing
                        spline method of Cox 1985 and Ng 1994.
z_correlated_normal     Generate n copies of Z ~ N_p(0,Sigma), where
                        Sigma_jj = 1, Sigma_jk = corr for all j not
                        equal to k.
