Package index • couplr

LAP Solving

Core functions for solving linear assignment problems

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
assignment(): Linear assignment solver
assignment_duals(): Solve assignment problem and return dual variables
bottleneck_assignment(): Solve the Bottleneck Assignment Problem
sinkhorn(): 'Sinkhorn-Knopp' optimal transport solver
sinkhorn_to_assignment(): Round 'Sinkhorn' transport plan to hard assignment

Matching Functions

High-level matching for observational studies

match_couples(): Optimal matching using linear assignment
greedy_couples(): Fast approximate matching using greedy algorithm
matchmaker(): 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(): Balance Diagnostics for Matched Pairs
balance_table(): Create Balance Table
sensitivity_analysis(): Rosenbaum Sensitivity Analysis

Distance and Preprocessing

Distance computation and data preparation

compute_distances(): Compute and Cache Distance Matrix for Reuse
update_constraints(): Update Constraints on Distance Object
preprocess_matching_vars(): Preprocess matching variables with automatic checks and scaling
diagnose_distance_matrix(): Diagnose distance matrix and suggest fixes

Joined Datasets & Output

Create analysis-ready merged datasets

join_matched(): Join Matched Pairs with Original Data
match_data(): Extract Analysis-Ready Data from Matching Results
augment(<matching_result>): Augment Matching Results with Original Data (broom-style)
augment(): Generic Augment Function

Ecosystem Integration

Interoperability with MatchIt, cobalt, and marginaleffects

as_matchit(): Convert couplr Result to matchit Object
bal.tab.matching_result() bal.tab.full_matching_result() bal.tab.cem_result() bal.tab.subclass_result(): Balance Table for Matching Results (cobalt integration)

Utility Functions

Helper functions for working with results

get_total_cost(): Extract total cost from assignment result
get_method_used(): Extract method used from assignment result
as_assignment_matrix(): Convert assignment result to a binary matrix
is_lap_solve_result(): Check if object is an assignment result
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_distance_object(): Check if Object is a Distance Object

Example Data

Built-in datasets for examples and testing

hospital_staff: Hospital staff scheduling example dataset
example_costs: Example cost matrices for assignment problems
example_df: Example assignment problem data frame

Pixel Morphing

Visual demonstrations and image processing

pixel_morph(): Pixel-level image morphing (final frame only)
pixel_morph_animate(): Pixel-level image morphing (animation)

Print, Summary and Plot Methods

S3 methods for displaying and visualizing results

print(<lap_solve_result>): Print method for assignment results
print(<lap_solve_batch_result>): Print method for batch assignment results
print(<lap_solve_kbest_result>): Print method for k-best assignment results
print(<matching_result>): Print method for matching results
print(<matchmaker_result>): Print method for matchmaker results
print(<balance_diagnostics>): Print Method for Balance Diagnostics
print(<distance_object>): Print Method for Distance Objects
print(<preprocessing_result>): Print method for preprocessing result
print(<full_matching_result>): Print Method for Full Matching Results
print(<cem_result>): Print Method for CEM Results
print(<subclass_result>): Print Method for Subclassification Results
print(<variable_health>): Print method for variable health
summary(<lap_solve_kbest_result>): Get summary of k-best results
summary(<distance_object>): Summary Method for Distance Objects
summary(<matching_result>): Summary method for matching results
summary(<balance_diagnostics>): Summary method for balance diagnostics
summary(<sensitivity_analysis>): Summary method for sensitivity analysis
plot(<matching_result>): Plot method for matching results
plot(<balance_diagnostics>): Plot method for balance diagnostics
plot(<sensitivity_analysis>): Plot method for sensitivity analysis (base graphics)
print(<sensitivity_analysis>): Print method for sensitivity analysis
autoplot(<balance_diagnostics>): ggplot2 autoplot for balance diagnostics
autoplot(<matching_result>): ggplot2 autoplot for matching results
autoplot(<sensitivity_analysis>): ggplot2 autoplot for sensitivity analysis