Package index • ineqTrees

All functions

as_caseweights(): Convert non-negative weights to integer case weights
best_factor_split(): Find the best factor split by concentration-index gain
best_global_ci_split(): Find the globally best greedy concentration-index split
best_numeric_split(): Find the best numeric split by concentration-index gain
best_split_for_one_variable(): Find the best CI split for one variable
ci_brier_score(): Compute weighted Brier score
ci_collect_extracts(): Collect saved extraction results
ci_collect_fits(): Collect saved fold-level fits
ci_collect_metrics(): Collect tuning metrics
ci_collect_notes(): Collect tuning notes
ci_collect_predictions(): Collect saved validation predictions
ci_control_from_row(): Convert a tuning row to CI tree controls
ci_cv_folds(): Create cross-validation folds
ci_dials_grid(): Convert a dials-style grid to greedy CI controls
ci_factory(): Create concentration-index scoring functions
ci_fit_summary_table(): Summarize selected CI tuning fits
ci_forest() cf_ci(): Fit a greedy concentration-index forest
ci_forest_parsnip(): Fit a greedy CI forest from parsnip
ci_forest_summary(): Summarize a greedy concentration-index forest
ci_forest_surrogate(): Fit a surrogate CI tree for a CI forest
ci_forest_validation_gain(): Compute held-out concentration-index validation gain for a CI forest
ci_gain(): Concentration-index gain metric
ci_gain_vec(): Concentration-index gain for a prediction partition
ci_log_loss(): Compute weighted log loss
ci_prediction_index(): Concentration-index prediction metric
ci_prediction_index_vec(): Concentration index of predictions
ci_prediction_metrics(): Compute common weighted prediction metrics
ci_roc_auc(): Compute weighted ROC AUC
ci_root_impurity(): Compute root concentration-index impurity
ci_select_best(): Select the best tuning parameters
ci_show_best(): Show the best tuning results
best_numeric_split_cpp() best_factor_split_cpp() best_split_for_one_variable_cpp() best_global_ci_split_cpp(): Greedy CI split search using Rcpp engines
ci_splitfun(): Create a concentration-index split function
ci_tree() ctree_ci(): Fit a greedy concentration-index tree
ci_tree_control(): Create controls for greedy concentration-index trees
ci_tree_control_grid(): Create a tuning grid for greedy concentration-index trees and forests
ci_tree_parsnip(): Fit a greedy CI tree from parsnip
ci_tree_terminal_summary(): Summarize terminal nodes from a greedy concentration-index tree
ci_tree_validation_gain(): Compute held-out concentration-index validation gain
ci_tuning_metric_set(): Create tidymodels tuning metrics for concentration-index model selection
control_ci_tune(): Control cross-validation for greedy CI model tuning
.assert_data_table_available(): Assert that the data.table package is available
.assert_scalar_flag(): Assert that an input is a single logical value
.assert_scalar_numeric(): Assert that an input is a single finite numeric value
.build_ci_tree(): Recursively build a greedy concentration-index party tree
.ci_node_info(): Build descriptive information for one greedy CI tree node
.ci_tree_normalize_control(): Normalize controls for greedy concentration-index trees
.ci_tree_response(): Recover the two-column response for a greedy CI tree
.dalex_shap_to_wide(): Reshape a DALEX-like long SHAP table to wide format
.prepare_named_shap_dt(): Prepare a SHAP table with named numeric columns Ensure that the input SHAP table is a data.table with named numeric columns. If the input is coercible to a data.table but has no column names, assign default names like "feature_1", "feature_2", etc. Validate that all columns are numeric and optionally check for missing values.
.validate_shap_table(): Validate a SHAP table Ensure that the input SHAP table is a data.table with at least one row and one numeric column. Optionally check for missing values and trigger an error any are found.
fitted(<ci_forest>): Extract fitted values from a greedy concentration-index forest
fractional_rank(): Compute fractional ranks
kenya: Simulated Kenya Child Survival Dataset
knit_print(<ci_forest>): Knit-print a greedy concentration-index forest
knit_print(<ci_tree>): Knit-print a greedy concentration-index tree
plot(<ci_tree>): Plot a greedy concentration-index tree with labeled split and node panels
predict(<ci_forest>): Predict from a greedy concentration-index forest
predict_ci_tree_terminal_mean() predict_ctree_ci_terminal_mean(): Predict terminal-node outcome means from a greedy CI tree
print(<ci_forest>): Print a greedy concentration-index forest
print(<ci_tree>): Print a greedy concentration-index tree
rank_wt(): Compute weighted fractional ranks
register_ineqtrees_decision_tree(): Register the decision_tree() ineqTrees engine
register_ineqtrees_parsnip(): Register ineqTrees parsnip engines
register_ineqtrees_rand_forest(): Register the rand_forest() ineqTrees engine
shap_conc_decomp(): Decompose a concentration index using SHAP values
tree_build_terminal_stats(): Build terminal-node summary statistics for a fitted tree
tree_compact_split_label(): Build a compact display label for a tree split
tree_edge_panel_compact(): Create a compact edge-panel generator for tree plots
tree_inner_panel_labeled(): Create a labeled inner-panel generator for tree plots
tree_pretty_var_name(): Choose a display label for a tree variable
tree_terminal_panel_stats(): Create a terminal-panel generator for tree plots with node summaries
tune_cf_ci() tune_ci_forest(): Tune greedy concentration-index forest controls by cross-validation
tune_ci_tree() tune_ctree_ci(): Tune greedy concentration-index tree controls by cross-validation
weighted_ci_gain(): Compute weighted concentration-index gain for a binary split