standardized_difference

Computes the standardized mean difference between two groups.
This is a key metric for assessing balance in matched samples.

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

standardized_difference function

<dl><dt>x1</dt>
<dd>Numeric vector for group 1</dd>
<dt>x2</dt>
<dd>Numeric vector for group 2</dd>
<dt>pooled</dt>
<dd>Logical, if TRUE use pooled standard deviation (default),
if FALSE use group 1 standard deviation</dd></dl>

Arguments

Calculate Standardized Difference — standardized_difference

<dl>

<dt>x1</dt>
<dd>Numeric vector for group 1</dd>


<dt>x2</dt>
<dd>Numeric vector for group 2</dd>


<dt>pooled</dt>
<dd>Logical, if TRUE use pooled standard deviation (default),
if FALSE use group 1 standard deviation</dd>

</dl>

standardized_difference: Calculate Standardized Difference

Description

Usage

Value

Arguments

Details