quickblock provides functions for assigning treatments in randomized experiments using near-optimal threshold blocking. The package is made with large data sets in mind and derives blocks more than an order of magnitude quicker than other methods.
How to install
quickblock is on CRAN and can be installed by running:
How to install development version
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/quickblock")
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.
Example on how to use quickblock
# Load package library("quickblock") # Construct example data my_data <- data.frame(x1 = runif(100), x2 = runif(100)) # Make distances to be used when making blocking my_distances <- distances(my_data, dist_variables = c("x1", "x2")) # Make blocking with at least four units in each block my_blocking <- quickblock(my_distances, size_constraint = 4L) # Two treatment conditions my_treatments <- assign_treatment(my_blocking, treatments = c("T", "C")) # Run experiment my_outcomes <- my_data$x1 + (my_treatments == "T") * my_data$x2 + rnorm(100) # Estimate treatment effects and variance blocking_estimator(my_outcomes, my_blocking, my_treatments)