OApackage (Orthogonal Array Package) review
A focused toolkit for constructing and evaluating orthogonal and optimal designs.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
OApackage is an open-source Python/C++ package for researchers and practitioners working with orthogonal arrays, conference designs and optimal experimental designs. Its scope is experimental design: it can generate and extend orthogonal-array lists, enumerate arrays, reduce them to normal form, and analyse statistical and structural properties. That makes it a fit for people constructing or assessing designs, rather than those seeking a general-purpose package for hypothesis testing or regression procedures.
Its analytical depth is centered on design quality and structure. OApackage constructs model matrices for main effects and interactions, calculates GWLP, F-values and J-characteristics, and reports D-, Ds-, A- and E-efficiency criteria. It also supports projection-estimation and information-capacity sequences. For design generation, it can produce conference and double-conference designs, plus D-efficient designs using coordinate-exchange optimization. These functions make the package relevant when array construction and evaluation are the core task; they do not make it a substitute for broader statistical workflows.
The package is free and open source, with desktop availability for Windows and Linux. Python bindings and command-line tools provide two ways to work with arrays, including utilities to check, join, split and analyse files. The supported array formats are text and binary .oa files, including gzip-compressed files, so the file workflow is specialized. Documentation is the listed support channel. Choose OApackage if orthogonal-array design, efficiency measures and related combinatorial design work are central to your research. If you need general-purpose statistical tests, regression models or a broader data-analysis environment, its focused design remit points toward another tool.
OApackage (Orthogonal Array Package) pros and cons
- Where it wins
- Generates orthogonal arrays, conference designs and D-efficient designs
- Calculates multiple design-efficiency criteria and structural measures
- Includes Python bindings and command-line array utilities
- Where it doesn't
- Specializes in experimental-design arrays rather than general-purpose analysis
- Works with .oa text and binary array files, including gzip-compressed files
- Documentation is the listed support channel
OApackage (Orthogonal Array Package) fact sheet, pricing and score →
Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.
Last updated · How we research and update