| 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. |
| MC_sums | Compute sums of a Monte Carlo vector for use in resmoothing. |
| 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. |