OApackage (Orthogonal Array Package)

Spec sheet

Purpose

OApackage generates and analyses orthogonal arrays, optimal designs, and conference designs.Read 9 Oct 2026 · oapackage.readthedocs.io

Platforms

The documentation says the package is continuously tested on Linux and Windows.Read 9 Oct 2026 · oapackage.readthedocs.io

Deployment

desktopRead 23 Sep 2026 · oapackage.readthedocs.io

Free plan

YesRead 23 Sep 2026 · oapackage.readthedocs.io

Support

The project directs contributors and users to its GitHub CONTRIBUTING.md file for contribution, unit-testing, and support information.Read 9 Oct 2026 · pypi.org

License

The package is distributed under a BSD-style license; PyPI identifies the license as BSD-3-Clause.Read 9 Oct 2026 · pypi.org

Analysis

It calculates structural and statistical properties including array rank, generalized wordlength pattern, F-values, J-characteristics, and design efficiencies.Read 9 Oct 2026 · oapackage.readthedocs.io

Array analysis

For orthogonal arrays, it can calculate generalized word length patterns, F-vectors, and J-characteristics.Read 9 Oct 2026 · oapackage.readthedocs.io

Bayesian analysis

NoRead 23 Sep 2026 · oapackage.readthedocs.io

D-efficient designs

The coordinate-exchange algorithm generates designs that optimize D-efficiency, but the documentation says there is no guarantee they have the largest possible D-efficiency.Read 9 Oct 2026 · oapackage.readthedocs.io

Data handling

Arrays can be stored in readable text or binary formats, and most tools transparently read compressed binary array files; writing compressed files is not supported.Read 9 Oct 2026 · github.com

Data import formats

.oa text and binary array files; gzip-compressed files supportedRead 23 Sep 2026 · oapackage.readthedocs.io

Dependencies

PyPI specifies Python 3.10 or newer and lists NumPy, python-dateutil, and Matplotlib as dependencies.Read 9 Oct 2026 · pypi.org

Generation

It uses exhaustive enumeration and optimization algorithms to generate arrays and designs.Read 9 Oct 2026 · oapackage.readthedocs.io

Installation

The Python interface can be installed with pip, and the command-line tools and C++ library can be built with CMake.Read 9 Oct 2026 · oapackage.readthedocs.io

Intended audience

PyPI classifies the project for science and research audiences.Read 9 Oct 2026 · pypi.org

Interfaces

The package provides C++ and Python interfaces, command-line tools, and a Matlab interface for generating optimal designs.Read 9 Oct 2026 · oapackage.readthedocs.io

Maintainer

PyPI names Pieter Eendebak as the author and lists the account eendebakpt as the maintainer.Read 9 Oct 2026 · pypi.org

Normal forms

The package includes functionality for reducing arrays to normal form and testing isomorphism.Read 9 Oct 2026 · github.com

Optimal designs

Its coordinate-exchange algorithm generates D-efficient designs and can optimize weighted combinations of D-, Ds-, and D1-efficiency criteria.Read 9 Oct 2026 · oapackage.readthedocs.io

R interface

An R interface to the optimal-design functionality is available from CRAN.Read 9 Oct 2026 · oapackage.readthedocs.io

Regression models

NoRead 23 Sep 2026 · oapackage.readthedocs.io

Scripting support

YesRead 23 Sep 2026 · oapackage.readthedocs.io

Statistical tests

NoRead 23 Sep 2026 · oapackage.readthedocs.io

Supported platforms

The documentation says the package is continuously tested on Linux and Windows; PyPI lists Windows x86-64 wheels for Python 3.10 through 3.14.Read 9 Oct 2026 · pypi.org

Plans and prices

Every OApackage (Orthogonal Array Package) plan and price →

Where Specifiction places it

• Statistical Analysis Software#26 of 1068.3