EDI: Experimental Design and Inference EDI (Experimental Design and Inference) hex logo

EDI (Experimental Design and Inference) marries experimental designs (fixed and sequential) with inference procedures (exact, asymptotic, and distribution-free) tailored to each design and response type: continuous, incidence, count, proportion, survival with left/right censoring, and ordinal. Designs, inference, and Monte Carlo simulation are exposed as R6 classes; the core estimation and variance-computing kernels are written in C++ (Eigen + LBFGS++) for speed.

EDI is not related to Electronic Data Interchange (the business-document exchange standard) or to equity, diversity, and inclusion; it is a statistics package for the design and analysis of randomized experiments.

Installation

Requires R >= 3.5.0 (R >= 4.0.0 recommended for best performance — newer Rtools toolchain on Windows, interpreter-side speedups). Install the released version from CRAN:

install.packages("EDI")

Prebuilt binaries of the latest tagged release (Linux, macOS, and Windows, no compiler toolchain needed) are also available from Adam Kapelner's R-universe:

install.packages(
  "EDI",
  repos = c(
    kapelner = "https://kapelner.r-universe.dev",
    CRAN = "https://cloud.r-project.org"
  )
)

Or install the development version straight from GitHub without cloning (requires a C++ compiler toolchain for R packages, e.g. Rtools on Windows, Xcode command line tools on macOS, or r-base-dev on Debian/Ubuntu — this package lives in the R/EDI subdirectory of the repository). Building from source like this (or from a local clone, below) automatically compiles with your machine's native CPU optimizations (-march=native, -O3) for extra speed — no flags to set yourself. The CRAN and R-universe installs above use a portable build instead, since those binaries have to run on hardware they were never compiled on:

remotes::install_github("kapelner/EDI", subdir = "R/EDI")

Or from a local clone:

# from the repository root
install.packages("R/EDI", repos = NULL, type = "source")

Getting started

library(EDI)
vignette("reproducibility", package = "EDI")      # RNG/seed conventions across designs, bootstrap, and simulation
vignette("extending-edi", package = "EDI")        # writing your own Design/Inference R6 subclasses
vignette("backend-contracts", package = "EDI")    # how the C++ core is shared between the R (Rcpp) and Python (pybind11) bindings
vignette("notation-glossary", package = "EDI")    # symbols/naming conventions shared across Design*/Inference* classes and docs
vignette("validation-evidence", package = "EDI")  # index into the test suite showing each model family computes what it claims

See the repository README for worked examples (fixed and sequential designs, the inference suite, design bakeoffs via SimulationFramework), local performance tuning, and the companion Python package edi_kernels.