couplr

Optimal Pairing and Matching via Linear Assignment

The couplr package provides high-level functions for optimal one-to-one matching between two groups. Whether you need to pair treatment and control units, assign workers to tasks, or align images pixel-by-pixel, couplr offers fast, deterministic solutions with automatic preprocessing and balance diagnostics.

Quick Start

library(couplr)

# Match treatment and control groups on covariates
result <- match_couples(
  treated, control,
  vars = c("age", "income", "education"),
  auto_scale = TRUE
)

# Check covariate balance
balance_diagnostics(result, treated, control, vars = c("age", "income", "education"))

# Get analysis-ready dataset
matched_data <- join_matched(result, treated, control)

Statement of Need

Optimal matching is central to experimental design, causal inference, and resource allocation. Existing R packages (MatchIt, optmatch) focus on propensity score workflows, requiring users to estimate scores before matching. This adds complexity and can obscure the direct relationship between covariates and match quality.

This package addresses direct covariate matching: selecting optimal pairs based on observed variables without intermediate modeling. It provides:

• 18 LAP algorithms for different problem sizes and structures,
• automatic preprocessing with variable health checks,
• balance diagnostics for assessing match quality,
• analysis-ready joined output.

These features make the package useful in domains like:

• causal inference (matching treated/control units),
• experimental design (pairing samples for within-pair comparisons),
• resource allocation (assigning workers to tasks),
• image processing (pixel-level morphing and alignment).

Features

High-Level Matching Functions

• match_couples(): Optimal one-to-one matching
  • Automatic preprocessing with variable health checks
  • Multiple scaling methods: robust (MAD), standardize (SD), range
  • Distance constraints via max_distance and calipers
  • Blocking support for stratified matching
• greedy_couples(): Fast approximate matching
  • Three strategies: sorted, row_best, pq (priority queue)
  • 10-100x faster than optimal for large datasets
  • Same preprocessing and constraint options

Balance Diagnostics

• balance_diagnostics(): Comprehensive balance assessment
  • Standardized differences, variance ratios, KS tests
  • Quality thresholds: <0.1 excellent, 0.1-0.25 good, 0.25-0.5 acceptable
  • Per-block statistics when blocking is used
  • Publication-ready tables via balance_table()

Low-Level LAP Solving

• lap_solve(): Tidy interface for LAP algorithms
  • 18 solvers: Hungarian, Jonker-Volgenant, Auction, Network Simplex, etc.
  • Automatic method selection via method = "auto"
  • Supports rectangular matrices and forbidden assignments
• lap_solve_batch(): Batch solving for multiple matrices
• lap_solve_kbest(): K-best solutions via Murty’s algorithm

Installation

# Install from CRAN
install.packages("couplr")

# Or install development version from GitHub
# install.packages("pak")
pak::pak("gcol33/couplr")

Usage Examples

Optimal Matching (match_couples)

library(couplr)

# Basic matching with automatic scaling
result <- match_couples(
  treated, control,
  vars = c("age", "income"),
  auto_scale = TRUE
)

# With distance constraint
result <- match_couples(
  treated, control,
  vars = c("age", "income"),
  auto_scale = TRUE,
  max_distance = 0.5
)

# With blocking (exact matching on site)
result <- match_couples(
  treated, control,
  vars = c("age", "income"),
  block_by = "site",
  auto_scale = TRUE
)

# Check what was matched
result$pairs

Greedy Matching (greedy_couples)

# Fast matching for large datasets
result <- greedy_couples(
  treated, control,
  vars = c("age", "income"),
  strategy = "row_best",
  auto_scale = TRUE
)

# Priority queue strategy (often best quality)
result <- greedy_couples(
  treated, control,
  vars = c("age", "income"),
  strategy = "pq"
)

Low-Level LAP Solving

# Solve a cost matrix
cost <- matrix(c(4, 2, 8, 4, 3, 7, 3, 1, 6), nrow = 3, byrow = TRUE)
result <- lap_solve(cost)
result$assignment
result$total_cost

# Choose a specific algorithm
result <- lap_solve(cost, method = "hungarian")

# K-best solutions
results <- lap_solve_kbest(cost, k = 3)

Choosing Between match_couples and greedy_couples

Feature	match_couples()	greedy_couples()
Optimality	Guaranteed optimal	Approximate
Speed	O(n^3)	O(n^2) or better
Best for	n < 5000	n > 5000
Supports constraints?	Yes	Yes
Supports blocking?	Yes	Yes

Tip: Start with match_couples(). Switch to greedy_couples() if runtime is too long.

Advanced Features

Distance Caching

Precompute distances for rapid experimentation:

# Compute once
dist_obj <- compute_distances(treated, control, vars = c("age", "income"))

# Reuse with different constraints
result1 <- match_couples(dist_obj, max_distance = 0.3)
result2 <- match_couples(dist_obj, max_distance = 0.5)

Parallel Processing

Speed up blocked matching with multi-core processing:

result <- match_couples(
  treated, control,
  vars = c("age", "income"),
  block_by = "site",
  parallel = TRUE
)

Pixel Morphing

Align images pixel-by-pixel using optimal assignment:

morph <- pixel_morph(image_a, image_b)
pixel_morph_animate(morph, "output.gif")

Documentation

• Getting Started
• Algorithm Details
• Matching Workflows
• Pixel Morphing

Support

“Software is like sex: it’s better when it’s free.” — Linus Torvalds

I’m a PhD student who builds R packages in my free time because I believe good tools should be free and open. I started these projects for my own work and figured others might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks. It helps with my coffee addiction.

License

MIT (see the LICENSE file)

Citation

@software{couplr,
  author = {Colling, Gilles},
  title = {couplr: Optimal Matching via Linear Assignment},
  year = {2026},
  url = {https://github.com/gcol33/couplr}
}