Package index • couplr

LAP Solving

Core functions for solving linear assignment problems

lap_solve(): Solve Linear Assignment Problems with a Tidy Interface
lap_solve_batch() print(<lap_solve_batch_result>) summary(<lap_solve_batch_result>) is_lap_solve_batch_result(): Solve Many Assignment Problems
lap_solve_kbest() print(<lap_solve_kbest_result>) summary(<lap_solve_kbest_result>) is_lap_solve_kbest_result(): Find the k Best Assignments
lap_solve_line_metric() summary(<lap_line_metric_result>): Solve a One-Dimensional Assignment Problem
lap_solvers() register_lap_solver(): Assignment Solvers
assignment() print(<lap_solve_result>) summary(<lap_solve_result>) is_lap_solve_result() get_total_cost() get_method_used(): Solve a Linear Assignment Problem
assignment_duals() print(<assignment_duals_result>) summary(<assignment_duals_result>) as.data.frame(<assignment_duals_result>): Solve an Assignment Problem and Return Dual Potentials
bottleneck_assignment() summary(<bottleneck_result>): Solve the Bottleneck Assignment Problem
sinkhorn() print(<sinkhorn_result>) summary(<sinkhorn_result>) as.matrix(<sinkhorn_result>): Entropic Optimal Transport by Sinkhorn-Knopp Iteration
sinkhorn_to_assignment(): Round a Sinkhorn Transport Plan to an Assignment

Matching Functions

High-level matching for observational studies

match_couples(): Optimal and Greedy Pair Matching
match_path() print(<couplr_path>) summary(<couplr_path>) as.data.frame(<couplr_path>) plot(<couplr_path>): Solve a Sequence of Matchings over One Design Parameter
matchmaker() print(<matchmaker_result>) summary(<matchmaker_result>): Create Blocks for Stratified Matching
ps_match(): Propensity Score Matching
cardinality_match(): Cardinality Matching
full_match(): Full Matching
cem_match(): Coarsened Exact Matching
subclass_match(): Subclassification on Propensity Score

Balance Diagnostics & Analysis

Assess match quality and sensitivity to hidden bias

balance_diagnostics() print(<balance_diagnostics>) summary(<balance_diagnostics>) plot(<balance_diagnostics>) autoplot(<balance_diagnostics>): Balance Diagnostics for Matched Samples
balance_table(): Balance Table of a Balance Diagnostics Object
autoplot(<sensitivity_analysis>) sensitivity_analysis() print(<sensitivity_analysis>) summary(<sensitivity_analysis>) as.data.frame(<sensitivity_analysis>) plot(<sensitivity_analysis>): Rosenbaum Sensitivity Analysis

Optimality Verification

Check a solution against the dual prices that certify it

verify_assignment() print(<assignment_certificate>) summary(<assignment_certificate>): Verify That an Assignment Is Optimal
verify_flow() print(<flow_certificate>) summary(<flow_certificate>): Verify That a Flow Is Optimal
certificate() potentials() unmatched_units() print(<cardinality_certificate>) summary(<cardinality_certificate>) glance(<cardinality_certificate>) glance(<assignment_certificate>) glance(<flow_certificate>): Certificate, Potentials and Unmatched Units of a Result

Distance and Preprocessing

Distance computation and data preparation

compute_distances() is_distance_object() print(<distance_object>) summary(<distance_object>) as.matrix(<distance_object>): Compute and Cache a Distance Matrix
update_constraints(): Update the Constraints of a Distance Object
preprocess_matching_vars() print(<variable_health>) summary(<variable_health>) print(<preprocessing_result>) summary(<preprocessing_result>): Check Matching Variables and Choose Scaling
diagnose_distance_matrix() print(<distance_diagnostics>) summary(<distance_diagnostics>): Diagnose a Distance Matrix
estimate_dense_matrix_mb(): Estimate the Memory of a Dense Cost Matrix
estimate_dense_solve_mb(): Estimate the Peak Memory of a Dense Solve

Joined Datasets & Output

Create analysis-ready merged datasets

join_matched() augment(<couplr_result>): Join Matched Pairs with the Original Data
match_data(): Extract Analysis-Ready Data from a Matching Result

Ecosystem Integration

Interoperability with MatchIt, cobalt, and marginaleffects

as_matchit(): Convert a Matching Result to a matchit Object
bal.tab(<couplr_result>) bal.tab(<subclass_result>): Balance Tables from cobalt for Matching Results

Utility Functions

Helper functions for working with results

assignment() print(<lap_solve_result>) summary(<lap_solve_result>) is_lap_solve_result() get_total_cost() get_method_used(): Solve a Linear Assignment Problem
explain_dispatch() print(<dispatch_explanation>) summary(<dispatch_explanation>): Explain the Automatic Solver Choice
solver_status_values(): Solver Status Values
as_assignment_matrix(): Convert an Assignment Result to a Binary Matrix
lap_solve_batch() print(<lap_solve_batch_result>) summary(<lap_solve_batch_result>) is_lap_solve_batch_result(): Solve Many Assignment Problems
lap_solve_kbest() print(<lap_solve_kbest_result>) summary(<lap_solve_kbest_result>) is_lap_solve_kbest_result(): Find the k Best Assignments
compute_distances() is_distance_object() print(<distance_object>) summary(<distance_object>) as.matrix(<distance_object>): Compute and Cache a Distance Matrix

Example Data

Built-in datasets for examples and testing

hospital_staff: Hospital Staff Example Data
example_costs: Example Cost Matrices
example_df: Example Assignment Data Frame

Pixel Morphing

Visual demonstrations and image processing

pixel_morph(): Pixel-Level Image Morphing
pixel_morph_animate() print(<pixel_morph_animation>) summary(<pixel_morph_animation>): Animated Pixel-Level Image Morphing

Algorithm Animation

Step-by-step visualization of LAP algorithms

lap_animate(): Animate an Assignment Algorithm
animated_methods(): List Methods That Support Animation

Print, Summary and Plot Methods

S3 methods for displaying and visualizing results

assignment() print(<lap_solve_result>) summary(<lap_solve_result>) is_lap_solve_result() get_total_cost() get_method_used(): Solve a Linear Assignment Problem
lap_solve_batch() print(<lap_solve_batch_result>) summary(<lap_solve_batch_result>) is_lap_solve_batch_result(): Solve Many Assignment Problems
lap_solve_kbest() print(<lap_solve_kbest_result>) summary(<lap_solve_kbest_result>) is_lap_solve_kbest_result(): Find the k Best Assignments
plot(<matching_result>) autoplot(<matching_result>) glance(<couplr_result>) tidy(<couplr_result>) as.data.frame(<couplr_result>) print(<couplr_result>) summary(<couplr_result>): Matching Results
match_path() print(<couplr_path>) summary(<couplr_path>) as.data.frame(<couplr_path>) plot(<couplr_path>): Solve a Sequence of Matchings over One Design Parameter
matchmaker() print(<matchmaker_result>) summary(<matchmaker_result>): Create Blocks for Stratified Matching
balance_diagnostics() print(<balance_diagnostics>) summary(<balance_diagnostics>) plot(<balance_diagnostics>) autoplot(<balance_diagnostics>): Balance Diagnostics for Matched Samples
compute_distances() is_distance_object() print(<distance_object>) summary(<distance_object>) as.matrix(<distance_object>): Compute and Cache a Distance Matrix
preprocess_matching_vars() print(<variable_health>) summary(<variable_health>) print(<preprocessing_result>) summary(<preprocessing_result>): Check Matching Variables and Choose Scaling
autoplot(<sensitivity_analysis>) sensitivity_analysis() print(<sensitivity_analysis>) summary(<sensitivity_analysis>) as.data.frame(<sensitivity_analysis>) plot(<sensitivity_analysis>): Rosenbaum Sensitivity Analysis