| as_confusion_matrix | Convert a tidy confusion data frame to a 2x2 integer matrix |
| as_cta_candidates | Subset a data frame to the SDA-selected candidate columns |
| as_sda_anchor | Convert an object to an 'sda_anchor' |
| as_sda_anchor.data.frame | Convert an object to an 'sda_anchor' |
| as_sda_anchor.sda_fit | Convert an object to an 'sda_anchor' |
| auto_sda_plan | Dry-run planning and validation layer for SDA |
| cta_assign_endpoints | Assign observations to CTA terminal endpoints |
| cta_balance_effect_summary | CTA covariate balance evidence-interval summary |
| cta_balance_plot_data | Renderer-ready plot data for CTA covariate balance |
| cta_balance_table | Multivariate CTA covariate balance diagnostics |
| cta_confusion_matrix | Extract training confusion matrix from a fitted CTA tree |
| cta_confusion_table | Final selected tree training confusion table |
| cta_demo | CTA demonstration dataset |
| cta_descendant_family | MDSA descendant family for CTA |
| cta_d_stat | D statistic for a fitted CTA tree |
| cta_endpoint_counts | Per-endpoint class count table for a fitted CTA tree |
| cta_endpoint_denominators | Terminal endpoint denominators of a CTA tree |
| cta_endpoint_summary | Endpoint reporting summary for a fitted CTA tree |
| cta_endpoint_table | Canonical terminal endpoint map for a fitted CTA tree |
| cta_family_table | Tidy table of a CTA descendant family |
| cta_fit | Fit a Classification Tree Analysis (CTA) model (public wrapper) |
| cta_min_terminal_denom | Minimum terminal endpoint denominator of a CTA tree |
| cta_node_table | Canonical CTA node report table |
| cta_observation_weights | Assign per-observation CTA propensity weights |
| cta_ort_node_table | Node-level summary table for a fitted LORT (legacy name: cta_ort) |
| cta_plot_data | Extract layout data for plotting a CTA tree |
| cta_propensity_weights | Endpoint-level propensity-score weights for a fitted CTA tree |
| cta_staging_table | Staging table for a fitted CTA tree |
| cta_strata | Number of terminal leaf endpoints in a CTA tree |
| lort_fit | Fit a Locally Optimal Recursive Tree (LORT) |
| lort_index_path | LORT path from root to a given node index |
| lort_local_tree | Extract the local CTA model embedded at a LORT node |
| lort_path_table | Formatted path table for a LORT recursion path |
| lort_propensity_weights | LORT terminal strata propensity weights |
| myeloma | Myeloma gene-expression dataset (CTA benchmark) |
| novo_boot_ci | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| novo_boot_ci.cta_ort | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| novo_boot_ci.cta_tree | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| novo_boot_ci.default | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| novo_boot_ci.oda_fit | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| oda_balance_effect_table | ODA covariate balance evidence-interval table |
| oda_balance_plot_data | Renderer-ready plot data for univariate ODA covariate balance |
| oda_balance_table | Univariate ODA covariate balance diagnostics |
| oda_best_ordered_multiclass_partition | Select the best K-segment ordered partition by MegaODA spec: PRIMARY -> SECONDARY -> FIRST IDENTIFIED (enum order via tick()). |
| oda_clean_missing_codes | Replace missing-code values with NA |
| oda_confusion | Retrieve a confusion matrix from a fitted ODA model |
| oda_confusion_binary | Binary confusion table |
| oda_confusion_multiclass | Multiclass confusion matrix |
| oda_cta_fit | Fit a Classification Tree Analysis (CTA) model (internal engine) |
| oda_d_stat | Compute the D statistic for a fitted ODA model |
| oda_ess_from_mean | ESS from mean metric for a C-class problem |
| oda_ess_from_meanpac | Effect Strength for Sensitivity from mean PAC |
| oda_fit | Fit an ODA model |
| oda_infer_attr_types | Infer attribute types from a predictor data frame |
| oda_loo_multiclass_ordered | Leave-one-out cross-validation for ordered multiclass ODA. |
| oda_mc_p_value | Monte Carlo Fisher-randomization p-value with Clopper-Pearson early stopping. |
| oda_mean_pac | Mean PAC from sensitivity and specificity |
| oda_metrics | Retrieve scalar performance metrics from a fitted ODA model |
| oda_multiclass_unioda_core | Fit a univariate multiclass ODA model |
| oda_power | ODA power analysis via simulation |
| oda_predictions | Retrieve predictions from a fitted ODA model |
| oda_propensity_weights | ODA rule strata propensity weights |
| oda_readiness_check | Preflight readiness check for ODA / CTA analysis |
| oda_rule_predict | Apply a binary ODA rule to new data |
| oda_rule_predict_multiclass | Apply a multiclass ODA rule to new data |
| oda_sample_size | ODA minimum sample size via bisection |
| oda_univariate_core | Fit a univariate binary-class ODA model |
| oda_validate_group | Validate a class / group variable |
| oda_validate_weights | Validate a case weight vector |
| ort_plot_data | Renderer-independent layout data for a LORT composite tree |
| plot.cta_ort | Plot method for Locally Optimal Recursive Tree (LORT) |
| plot.cta_tree | Plot a fitted CTA tree |
| plot_balance_love | Love plot for covariate balance (SMD) |
| plot_cta_balance | Plot CTA multivariate covariate balance |
| plot_cta_balance_effects | Evidence card for CTA multivariate covariate balance |
| plot_cta_family | Plot a CTA descendant family member using ggplot2 |
| plot_cta_tree | Plot a CTA tree using ggplot2 |
| plot_lort_path | Plot the full local CTA models along a LORT recursion path |
| plot_lort_tree | Plot a LORT (Locally Optimal Recursive Tree) using ggplot2 |
| plot_oda_balance | Plot ODA covariate balance |
| plot_oda_balance_effects | Forest plot of ODA covariate balance evidence intervals |
| plot_smd_balance | Plot SMD covariate balance |
| predict.cta_ort | Predict method for Locally Optimal Recursive Tree (LORT) |
| predict.cta_tree | Classify new observations using a CTA tree |
| predict.oda_fit | Predict class labels from a fitted ODA model |
| predict.sda_fit | Predict from an SDA procedure result |
| print.auto_sda_plan | Print an auto_sda_plan object |
| print.cta_family | Print a CTA descendant family |
| print.cta_family_summary | Print a CTA family summary |
| print.cta_ort | Print method for Locally Optimal Recursive Tree (LORT) |
| print.cta_ort_summary | Print method for cta_ort_summary |
| print.cta_tree | Print a CTA tree in MegaODA node table format |
| print.cta_tree_summary | Print a CTA tree summary |
| print.novo_boot_ci | Novometric bootstrap CI from a fixed 2x2 confusion matrix |
| print.oda_fit | Print a fitted ODA model |
| print.oda_fit_summary | Print an ODA fit summary |
| print.sda_anchor | Print an 'sda_anchor' |
| print.sda_fit | Print an sda_fit object |
| print.sda_fit_summary | Print an sda_fit_summary object |
| propensity_ess_balance | Propensity-weighted ESS balance diagnostic |
| sda_anchor | Construct an 'sda_anchor' object |
| sda_candidate_table | Return the candidate table from one or all SDA steps |
| sda_fit | Run a Structural Decomposition Analysis (SDA) procedure |
| sda_selected_attributes | Return the selected attribute names from an SDA procedure result |
| sda_step_table | Return a summary table of SDA steps |
| sda_to_cta_data | Prepare X and y for CTA using SDA-selected attributes |
| smd_balance_table | Conventional SMD companion table for covariate balance |
| summary.cta_family | Summarise a CTA descendant family |
| summary.cta_ort | Summary method for Locally Optimal Recursive Tree (LORT) |
| summary.cta_tree | Summarize a fitted CTA tree |
| summary.oda_fit | Summarize a fitted ODA model |
| summary.sda_anchor | Summarise an 'sda_anchor' |
| summary.sda_fit | Summarise an sda_fit object |
| validate_sda_anchor | Validate an 'sda_anchor' object |
| .lort_parent_maps | Build parent map and endpoint-index map for LORT nodes (internal helper used by lort_index_path and lort_path_table) |