Directional Penalties for Optimal Matching in Observational Studies
Add a caliper, that need not be symmetric, to a distance object.
Add a directional penalty to a distance object
Add a directional magnitude penalty to a distance matrix
Check standardized mean differences (SMDs) of the matched data set.
Computes the number of edges in the reduced bipartite graph.
Creates a robust Mahalanobis distance for matching based on a dense ne...
Creates a robust Mahalanobis distance for matching based on a sparse n...
Minimum-distance near-fine matching.
Optimal near-fine match from a distance matrix.
Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.