Man pages for blind-contours/CVtreeMLE
Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation
| average_mixture_rules | Estimate the average rule. This is the rule that is the... |
| bound_precision | Bound Precision |
| bound_propensity | Bound Generalized Propensity Score |
| calc_ATE_estimates | Calculate the ATE and variance estimates |
| calc_clever_covariate | Calculate the Clever Covariate for the TMLE step of the ATE |
| calc_marginal_ate | Calculate the ATE for each rule found for individual mixture... |
| calc_marginal_rule_RMSEs | Calculate the mean RMSE in each marginal group |
| calc_mixture_rule_RMSEs | Calculate the mean RMSE in each interaction group |
| calc_mixtures_ate | Calculate the ATE for each mixture rule |
| calculatePooledEstimate | Calculates the Inverse Variance Pooled Estimate Including... |
| calc_v_fold_marginal_ate | Calculate the v-fold specifice ATE for each rule found for... |
| calc_v_fold_mixtures_ate | Calculate the ATE for V-fold specific rules |
| common_mixture_rules | Estimate the union rule. This is the rule that covers all... |
| compute_meta_marg_results | Compute v-fold specific estimates and do a meta-analysis type... |
| create_cv_folds | Stratified CV to insure balance (by one grouping variable, Y) |
| create_rules | From HAL fit, create string based rules |
| create_sls | Create default Super Learner estimators for the data adaptive... |
| CVtreeMLE | Fit ensemble decision trees to a vector of exposures and use... |
| est_comb_exposure | Estimate the expected outcome for the combination of marginal... |
| est_marg_nuisance_params | Estimate nuisance parameters for each marginal mixture... |
| est_mix_nuisance_params | Estimate nuisance parameters for each mixture interaction... |
| evaluate_marginal_rules | Evaluate mixture rules found during the rpart decision tree... |
| evaluate_mixture_rules | Evaluate mixture rules found during the PRE process |
| evaluate_rules_to_binary | Evaluate Rules to Binary Indicators |
| filter_marginal_rules | Filter marginal rules across the folds for only those that... |
| filter_mixture_rules | Filter mixture rules across the folds for only those that... |
| filter_rules | Filter data based on fold |
| find_common_marginal_rules | Create a new rule based on observations that meet every rule... |
| fit_least_fav_submodel | Least Favorable Submodel |
| fit_marg_rule_backfitting | Iteratively back-fit a Super Learner on marginal mixture... |
| fit_min_ave_tree_algorithm | Fit minimum average tree |
| fit_mix_rule_backfitting | Iteratively Backfit a Super Learner, h(x) = Y|W, and an... |
| groupby_fold | Group by fold |
| list_rules_party | Get rules from partykit object in rule fitting |
| marginal_group_split | Group split by marginal variable |
| meta_mix_results | Compute v-fold specific estimates and do a meta-analysis type... |
| NHANES_eurocim | NHANES 2001-2002, POP Exposure on Telomere Length |
| NIEHS_data_1 | Data 1 from the NIEHS mixtures workshop |
| plot_marginal_results | Create dot-whisker plots for the marginal results found |
| pull_out_rule_vars | Pull rules out of results table of pre results |
| round_rules | Round rules found for easier reading |
| scale_to_original | Transform Values From The Unit Interval Back To Their... |
| scale_to_unit | Transform values by scaling to the unit interval |
| simulate_mixture_cube | Simulate a mixture cube to test 'CVtreeMLE' against simulated... |
| v_fold_marginal_qgroup_split | v-fold marginal group split |
| v_fold_mixture_group_split | v-fold group split |
