quickmatch provides functions for constructing near-optimal generalized full matchings. Generalized full matching is an extension of the original full matching method to situations with more intricate study designs. The package is made with large data sets in mind and derives matchings more than an order of magnitude quicker than other methods.
quickmatch is on CRAN and can be installed by running:
It is recommended to use the stable CRAN version, but the latest development version can be installed directly from Github using devtools:
if (!require("devtools")) install.packages("devtools")
devtools::install_github("fsavje/quickmatch")The package contains compiled code, and you must have a development environment to install the development version. (Use devtools::has_devel() to check whether you do.) If no development environment exists, Windows users download and install Rtools and macOS users download and install Xcode.
# Load package
library("quickmatch")
# Construct example data
my_data <- data.frame(y = rnorm(100),
x1 = runif(100),
x2 = runif(100),
treatment = factor(sample(rep(c("T", "C"), c(25, 75)))))
# Make distances
my_distances <- distances(my_data, dist_variables = c("x1", "x2"))
### Average treatment effect (ATE)
# Make matching
my_matching_ate <- quickmatch(my_distances, my_data$treatment)
# Covariate balance
covariate_balance(my_data$treatment, my_data[c("x1", "x2")], my_matching_ate)
# Estimate effect
lm_match(my_data$y, my_data$treatment, my_matching_ate)
### Average treatment effect of the treated (ATT)
# Make matching
my_matching_att <- quickmatch(my_distances, my_data$treatment, target = "T")
# Covariate balance
covariate_balance(my_data$treatment, my_data[c("x1", "x2")], my_matching_att, target = "T")
# Estimate effect
lm_match(my_data$y, my_data$treatment, my_matching_att, target = "T")