setFeasibilityConstants

<p>(Internal) Sets up the default values for maximum feasible problems</p>

internal

Distance based bipartite matching using minimum cost flow, oriented
to matching of treatment and control groups in observational studies ('Hansen'
and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to
generate distances from generalised linear models (propensity score
matching), formulas giving variables on which to limit matched distances,
stratified or exact matching directives, or calipers, alone or in
combination.

Josh Errickson

optmatch

Functions for Optimal Matching

Ben Hansen

Mark Fredrickson

Josh Buckner

Adam Rauh

setFeasibilityConstants function

(Internal) Sets up the default values for maximum feasible problems — setFeasibilityConstants

(Internal) Sets up the default values for maximum feasible problems

setFeasibilityConstants: (Internal) Sets up the default values for maximum feasible problems

Description

Usage

Arguments