Quick Threshold Blocking.

quickblock

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:

install.packages("quickblock")

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)