R library OptimalDesign
- The library OptimalDesign
enables the user to easily compute D-, A-, I-, and c-efficient designs of experiments including:
- optimal approximate and exact designs with or without replications,
- optimal designs at standard or restricted design spaces,
- optimal designs under various kinds of simultaneous constraints, ...
- The library is free, including the free academic license for the few functions that require the commercial
gurobi solver; see our gurobi installation guide.
(Note that most of the computational procedures in the library OptimalDesign are open source and do not require the gurobi solver.)
- Many of the implemented algorithms have been developed by the authors of OptimalDesign themselves and substantially outperform competing methods and/or allow for solving
more complex design problems.
- See a brief introduction including mathematical specifications.
can also be used to compute the solutions to some related optimization problems such as
minimum-volume data-enclosing ellipsoids and t-optimal graphs.
- The package is actively maintained, continually tested and improved by the authors.
See the list of known bugs and suggestions for improvement.
- We will be happy to help you with any technical or theoretical problem,
or with any question related to the computation of experimental designs.
Computer source codes accompanying our academic papers
- Harman R, Filová L, Rosa S (2021): Optimal Design of Multifactor Experiments via Grid Exploration, to appear in Statistics and Computing
(see the arXiv preprint for a preliminary version).
- Filová L, Harman R (2020): Ascent with Quadratic Assistance for the Construction of Exact Experimental Designs,
Computational Statistics, Volume 35, pp. 775–801 DOI,
rdcu (pre-print: arXiv)
- Harman R, Filová L, Richtárik P (2020): A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments,
Journal of the American Statistical Association, Volume 115, pp. 348-361
DOI (pre-print: arXiv)
- Harman R, Benková E (2017): Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments,
Metrika, Volume 80, pp. 201–225 DOI,
- barycost.m (barycost.txt) for Matlab:
Compute D-optimal approximate size-and-cost constrained designs of experiments.
- Harman R, Bachratá A, Filová L (2016): Construction of efficient experimental designs under multiple resource constraints,
Applied Stochastic Models in Business and Industry, Volume 32, pp. 3-17 DOI,
arXiv (older version)
- Benková E, Harman R, Müller WG (2016): Privacy sets for constrained space-filling,
Journal of Statistical Planning and Inference, Volume 171, pp. 1–9
- Henry.m for Matlab - Experimental design optimization for "bridge" constraints
and the D-optimality criterion
- Harman R, Filová L (2014): Computing efficient exact designs of experiments using integer quadratic programming, Computational Statistics & Data Analysis, Volume 71, pp. 1159–1167
- DQ_gurobi.m (DQ_gurobi.txt) for Matlab,
using the Gurobi solver: Compute exact DQ-optimal designs of experiments under general linear constraints
on the design weights.
- DQ_cplex.m (DQ_cplex.txt) for Matlab,
using the Cplex solver: Compute exact DQ-optimal designs of experiments under general linear constraints
on the design weights.
- E-mail: harman[at]fmph.uniba.sk
Department of Applied Mathematics and Statistics
Faculty of Mathematics, Physics and Informatics
842 48 Bratislava 4