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DiPs (version 0.6.4)

Directional Penalties for Optimal Matching in Observational Studies

Description

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) .

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Install

install.packages('DiPs')

Monthly Downloads

315

Version

0.6.4

License

MIT + file LICENSE

Maintainer

Ruoqi Yu

Last Published

August 7th, 2022

Functions in DiPs (0.6.4)

check

Check standardized mean differences (SMDs) of the matched data set.
nh0506Homocysteine

Homocysteine and Smoking
edgenum

Computes the number of edges in the reduced bipartite graph.
match

Minimum-distance near-fine matching.
addcaliper

Add a caliper, that need not be symmetric, to a distance object.
addDirectPenalty

Add a directional penalty to a distance object
net

Optimal near-fine match from a distance matrix.
maha_sparse

Creates a robust Mahalanobis distance for matching based on a sparse network.
maha_dense

Creates a robust Mahalanobis distance for matching based on a dense network.
addMagnitudePenalty

Add a directional magnitude penalty to a distance matrix