couplr

matching two groups into pairs

Optimal one-to-one matching by linear assignment, solved exactly in C++.

Hand it two groups. couplr returns the pairing that minimizes total covariate distance across the whole sample, solved by linear assignment (Jonker-Volgenant, Hungarian, Auction, cost-scaling) on RcppEigen. The common tool, greedy nearest neighbour, locks in each pair as it goes and gives back whatever the ordering produced. This finds the global minimum, and the same answer every run.

library(couplr)

# match treatment and control on covariates, in a single call
result <- match_couples(treated, control, vars = c("age", "income"), auto_scale = TRUE)

# analysis-ready paired data
join_matched(result, treated, control)

Optimal, not greedy

MatchIt, the most used matching package in R, pairs units greedily on an estimated propensity score: it takes them in order, grabs the nearest free control for each, and the result depends on that order. couplr matches directly on the covariates and solves the assignment exactly, so total distance is the global minimum and the pairing is the same every time.

match_couples(treated, control, vars = c("age", "income"), auto_scale = TRUE)   # optimal, deterministic
greedy_couples(treated, control, vars = c("age", "income"), strategy = "pq")    # greedy, for large pools

For large control pools, greedy_couples() trades the exact guarantee for speed, with three strategies (sorted, row_best, pq) and the same preprocessing and constraints.

What's in the box

• match_couples(): optimal one-to-one matching with automatic scaling (robust / standardize / range), distance constraints (max_distance, calipers), blocking, and ratio / replace matching.
• greedy_couples(): fast approximate matching for large datasets, three strategies.
• full_match() / cem_match() / subclass_match() / cardinality_match(): variable-ratio full matching, coarsened exact matching, propensity subclassification, and balance-constrained matching.
• ps_match(): propensity score matching with a logit caliper.
• balance_diagnostics() / sensitivity_analysis(): standardized differences, variance ratios, KS tests, and Rosenbaum bounds for hidden bias.
• lap_solve(): tidy interface to the assignment backend, 20 solvers with method = "auto", plus lap_solve_batch() and lap_solve_kbest() (Murty's algorithm).

The assignment backend

lap_solve() exposes the solver layer directly. It takes a cost matrix, handles rectangular shapes and forbidden edges (NA / Inf), and picks an algorithm from the problem when method = "auto":

cost <- matrix(c(4, 2, 8, 4, 3, 7, 3, 1, 6), nrow = 3, byrow = TRUE)

lap_solve(cost)                      # auto-selected solver
lap_solve(cost, method = "hungarian")
lap_solve_kbest(cost, k = 3)         # the three best assignments

The solvers span the classics and the scaling algorithms: Jonker-Volgenant, Hungarian, Kuhn-Munkres, Bertsekas auction (with epsilon-scaling variants), Goldberg-Kennedy cost-scaling, Gabow-Tarjan bit-scaling, push-relabel, network simplex, and Sinkhorn entropy-regularized transport.

match_couples or greedy_couples?

	match_couples()	greedy_couples()
Result	Globally optimal	Approximate
Deterministic?	Yes	Yes
Cost	O(n^3)	O(n^2) or better
Best for	n < 5000	large control pools
Constraints, blocking?	Yes	Yes

Start with match_couples(). Switch to greedy_couples() when the optimal solve runs too long.

Fits the matching ecosystem

couplr results convert to matchit-class with as_matchit(), so cobalt balance tables and marginaleffects estimates work without rewiring your analysis. match_data() returns treatment, weights, and subclass columns in one analysis-ready frame, and autoplot() methods cover matching results, balance, and sensitivity.

Installation

install.packages("couplr")            # CRAN

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

Documentation

• Getting Started
• Matching Workflows
• Algorithms
• Comparison
• 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}
}