Man pages for couplr
Optimal Pairing and Matching via Linear Assignment
| apply_all_constraints | Apply all constraints to cost matrix |
| apply_calipers | Apply caliper constraints |
| apply_max_distance | Apply maximum distance constraint |
| apply_scaling | Apply scaling to matching variables |
| apply_weights | Apply weights to matching variables |
| as_assignment_matrix | Convert assignment result to a binary matrix |
| assign_blocks_cluster | Assign blocks using clustering |
| assign_blocks_group | Assign blocks based on grouping variable(s) |
| assignment | Linear assignment solver |
| assignment_duals | Solve assignment problem and return dual variables |
| augment | Generic Augment Function |
| augment.matching_result | Augment Matching Results with Original Data (broom-style) |
| auto_encode_categorical | Automatically encode categorical variables |
| balance_diagnostics | Balance Diagnostics for Matched Pairs |
| balance_table | Create Balance Table |
| BIG_COST | Large value for forbidden pairs |
| bottleneck_assignment | Solve the Bottleneck Assignment Problem |
| build_cost_matrix | Build cost matrix for matching |
| calculate_var_balance | Calculate Variable-Level Balance Statistics |
| can_parallelize | Check if parallel processing is available |
| check_cost_distribution | Check cost distribution for problems |
| check_full_matching | Check if full matching was achieved |
| check_variable_health | Check variable health for matching |
| compute_distance_matrix | Compute pairwise distance matrix |
| compute_distances | Compute and Cache Distance Matrix for Reuse |
| count_valid_pairs | Count valid pairs in cost matrix |
| couplr_emoji | Get a themed emoji |
| couplr_inform | Info message with emoji |
| couplr_messages | Couplr message helpers with emoji and humor |
| couplr-package | couplr: Optimal Pairing and Matching via Linear Assignment |
| couplr_stop | Stop with a fun, themed error message |
| couplr_success | Success message with emoji |
| couplr_warn | Warn with a fun, themed warning message |
| detect_blocking | Detect and validate blocking |
| diagnose_distance_matrix | Diagnose distance matrix and suggest fixes |
| err_invalid_param | Invalid parameter error |
| err_missing_data | Missing data error |
| err_missing_vars | Missing variables error |
| err_no_valid_pairs | All pairs forbidden error |
| example_costs | Example cost matrices for assignment problems |
| example_df | Example assignment problem data frame |
| extract_ids | Extract and standardize IDs from data frames |
| extract_matching_vars | Extract matching variables from data frame |
| filter_blocks | Filter blocks based on size and balance criteria |
| get_block_id_column | Standardize block ID column name |
| get_method_used | Extract method used from assignment result |
| get_total_cost | Extract total cost from assignment result |
| greedy_blocks_parallel | Greedy match blocks in parallel |
| greedy_couples | Fast approximate matching using greedy algorithm |
| greedy_couples_blocked | Greedy matching with blocking |
| greedy_couples_from_distance | Greedy Matching from Precomputed Distance Object |
| greedy_couples_single | Greedy matching without blocking |
| group_by | Re-export of dplyr::group_by |
| has_blocks | Check if data frame has blocking information |
| has_valid_pairs | Check if any valid pairs exist |
| hospital_staff | Hospital staff scheduling example dataset |
| info_low_match_rate | Low match rate info |
| is_distance_object | Check if Object is a Distance Object |
| is_lap_solve_batch_result | Check if object is a batch assignment result |
| is_lap_solve_kbest_result | Check if object is a k-best assignment result |
| is_lap_solve_result | Check if object is an assignment result |
| join_matched | Join Matched Pairs with Original Data |
| lap_solve | Solve linear assignment problems |
| lap_solve_batch | Solve multiple assignment problems efficiently |
| lap_solve_kbest | Find k-best optimal assignments |
| lap_solve_line_metric | Solve 1-D Line Assignment Problem |
| mark_forbidden_pairs | Mark forbidden pairs |
| match_blocks_parallel | Match blocks in parallel |
| match_couples | Optimal matching using linear assignment |
| match_couples_blocked | Match with blocking (multiple problems) |
| match_couples_from_distance | Match from Precomputed Distance Object |
| match_couples_single | Match without blocking (single problem) |
| matchmaker | Create blocks for stratified matching |
| parallel_lapply | Parallel lapply using future |
| pipe | Pipe operator |
| pixel_morph | Pixel-level image morphing (final frame only) |
| pixel_morph_animate | Pixel-level image morphing (animation) |
| plot.balance_diagnostics | Plot method for balance diagnostics |
| plot.matching_result | Plot method for matching results |
| preprocess_matching_vars | Preprocess matching variables with automatic checks and... |
| print.balance_diagnostics | Print Method for Balance Diagnostics |
| print.distance_object | Print Method for Distance Objects |
| print.lap_solve_batch_result | Print method for batch assignment results |
| print.lap_solve_kbest_result | Print method for k-best assignment results |
| print.lap_solve_result | Print method for assignment results |
| print.matching_result | Print method for matching results |
| print.matchmaker_result | Print method for matchmaker results |
| print.preprocessing_result | Print method for preprocessing result |
| print.variable_health | Print method for variable health |
| restore_parallel | Restore original parallel plan |
| setup_parallel | Setup parallel processing with future |
| sinkhorn | 'Sinkhorn-Knopp' optimal transport solver |
| sinkhorn_to_assignment | Round 'Sinkhorn' transport plan to hard assignment |
| standardized_difference | Calculate Standardized Difference |
| success_good_balance | Perfect balance success message |
| suggest_scaling | Suggest scaling method based on variable characteristics |
| summarize_blocks | Summarize block structure |
| summary.balance_diagnostics | Summary method for balance diagnostics |
| summary.distance_object | Summary Method for Distance Objects |
| summary.lap_solve_kbest_result | Get summary of k-best results |
| summary.matching_result | Summary method for matching results |
| update_constraints | Update Constraints on Distance Object |
| use_emoji | Check if emoji should be used |
| validate_calipers | Validate calipers parameter |
| validate_cost_data | Validate and prepare cost data |
| validate_matching_inputs | Validate matching inputs |
| validate_weights | Validate weights parameter |
| warn_constant_distance | All distances identical warning |
| warn_constant_var | Constant variable warning |
| warn_extreme_costs | Extreme cost ratio warning |
| warn_many_forbidden | Many forbidden pairs warning |
| warn_many_zeros | Too many zeros warning |
| warn_parallel_unavailable | Parallel package missing warning (reuse from... |
| warn_poor_quality | High distance matches warning |
