validate_cost_data

Internal helper that ensures a numeric, non-empty cost matrix.

internal

Solves optimal pairing and matching problems using linear assignment
algorithms. Provides implementations of the Hungarian method (Kuhn 1955)
<doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm
(Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm
(Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling
(Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms
(Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and
Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized
transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for
matching plots, sites, samples, or any pairwise optimization problem.
Supports rectangular matrices, forbidden assignments, data frame inputs,
batch solving, k-best solutions, and pixel-level image morphing for
visualization. Includes automatic preprocessing with variable health
checks, multiple scaling methods (standardized, range, robust), greedy
matching algorithms, and comprehensive balance diagnostics for assessing
match quality using standardized differences and distribution comparisons.

Gilles Colling

couplr

Optimal Pairing and Matching via Linear Assignment

validate_cost_data function

<dl><dt>x</dt>
<dd>Cost matrix or data frame</dd>
<dt>forbidden</dt>
<dd>Value representing forbidden assignments (use NA or Inf)</dd></dl>

Arguments

Validate and prepare cost data — validate_cost_data

<dl>

<dt>x</dt>
<dd>Cost matrix or data frame</dd>


<dt>forbidden</dt>
<dd>Value representing forbidden assignments (use NA or Inf)</dd>

</dl>

validate_cost_data: Validate and prepare cost data

Description

Usage

Value

Arguments