| add_aspre_interactions | Add interaction terms corresponding to ASPRE model |
| aspre | Computes ASPRE score |
| aspre_emulation | Emulation-based OHS estimation for ASPRE |
| aspre_k2 | Cost estimating function in ASPRE simulation |
| aspre_parametric | Parametric-based OHS estimation for ASPRE |
| ci_cover_a_yn | Data for example on asymptotic confidence interval for OHS. |
| ci_cover_cost_a_yn | Data for example on asymptotic confidence interval for min cost. |
| ci_cover_cost_e_yn | Data for example on empirical confidence interval for min cost. |
| ci_cover_e_yn | Data for example on empirical confidence interval for OHS. |
| ci_mincost | Confidence interval for minimum total cost, when estimated using parametric method |
| ci_ohs | Confidence interval for optimal holdout size, when estimated using parametric method |
| cov_fn | Covariance function for Gaussian process |
| data_example_simulation | Data for vignette showing general example |
| data_nextpoint_em | Data for 'next point' demonstration vignette on algorithm comparison using emulation algorithm |
| data_nextpoint_par | Data for 'next point' demonstration vignette on algorithm comparison using parametric algorithm |
| error_ohs_emulation | Measure of error for emulation-based OHS emulation |
| exp_imp_fn | Expected improvement |
| gen_base_coefs | Coefficients for imperfect risk score |
| gen_preds | Generate matrix of random observations |
| gen_resp | Generate response |
| grad_mincost_powerlaw | Gradient of minimum cost (power law) |
| grad_nstar_powerlaw | Gradient of optimal holdout size (power law) |
| logistic | Logistic |
| logit | Logit |
| model_predict | Make predictions |
| model_train | Train model (wrapper) |
| mu_fn | Updating function for mean. |
| next_n | Finds best value of n to sample next |
| ohs_array | Data for vignette on algorithm comparison |
| ohs_resample | Data for vignette on algorithm comparison |
| optimal_holdout_size | Estimate optimal holdout size under parametric assumptions |
| optimal_holdout_size_emulation | Estimate optimal holdout size under semi-parametric assumptions |
| oracle_pred | Generate responses |
| params_aspre | Parameters of reported ASPRE dataset |
| plot.optholdoutsize | Plot estimated cost function |
| plot.optholdoutsize_emul | Plot estimated cost function using emulation (semiparametric) |
| powerlaw | Power law function |
| powersolve | Fit power law curve |
| powersolve_general | General solver for power law curve |
| powersolve_se | Standard error matrix for learning curve parameters (power law) |
| psi_fn | Updating function for variance. |
| sens10 | Sensitivity at theshold quantile 10% |
| sim_random_aspre | Simulate random dataset similar to ASPRE training data |
| split_data | Split data |