# Pure-R Core Engine for Optimal Data Analysis (ODA / MultiODA)

## Documentation for package ‘oda’ version 0.1.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C L M N O P S V misc

## -- A --

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

## -- C --

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

## -- L --

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

## -- M --

myeloma | Myeloma gene-expression dataset (CTA benchmark)

## -- N --

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

## -- O --

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

## -- P --

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

## -- S --

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

## -- V --

validate_sda_anchor | Validate an 'sda_anchor' object

## -- misc --

.lort_parent_maps | Build parent map and endpoint-index map for LORT nodes (internal helper used by lort_index_path and lort_path_table)