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tightenBlock (version 0.1.7)

Tightens an Observational Block Design by Balanced Subset Matching

Description

Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 ) and two-criteria matching (Zhang et al 2023 ). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) , (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at , (iv) is not essential to use the package.

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Version

Install

install.packages('tightenBlock')

Monthly Downloads

112

Version

0.1.7

License

GPL-2

Maintainer

Paul Rosenbaum

Last Published

December 15th, 2023

Functions in tightenBlock (0.1.7)

startcost

Initialize a Distance Matrix.
addMahal

Rank-Based Mahalanobis Distance Matrix
makematch

Make a Match Using Two Criteria Matching with Optimal Subset Matching
aHDLt

Alcohol and HDL Cholesterol
tighten

Tightening an Observational Block Design
addNearExact

Add a Near-exact Penalty to an Exisiting Distance Matrix.
makenetwork

Make the Network Used for Matching with Two Criteria
tightenBlock-package

tools:::Rd_package_title("tightenBlock")