softDOE 0.1.0

Space-filling one-factor-at-a-time designs.

softDOE

softDOE is a Python package for constructing space-filling one-factor-at-a-time (SOFT) designs.

What is included

softDOE currently includes:

• soft(): generate SOFT designs.
• measure(): compute screening measures.

Generated designs are returned as NumPy arrays with entries in (0, 1).

Installation

Install softDOE from PyPI:

pip install softDOE

softDOE requires Python 3.9 or later. The package depends on NumPy, SciPy, and pyDOE, and uses a compiled C++ extension built with pybind11.

Quick example

import numpy as np
import softDOE

# SOFT design
design = softDOE.soft(p=10, l=4)

# Friedman function
def friedman(x):
    x = np.asarray(x)
    return (
        10 * np.sin(np.pi * x[0] * x[1])
        + 20 * (x[2] - 0.5) ** 2
        + 10 * x[3]
        + 5 * x[4]
    )

# Evaluate Friedman function at each design point.
y = np.array([friedman(row) for row in design])

# Sensitivity measures
result = softDOE.measure(design, y)

print(result["mustar"])
print(result["t"])

Generating SOFT designs

Use softDOE.soft() to generate a SOFT design:

design = softDOE.soft(p=5, l=4, structure="standard", m=500, seed=123)

Arguments:

• p: number of factors.
• l: number of base runs. This value must be even.
• structure: either "standard" or "strict". The default is "standard".
• m: number of Monte Carlo samples used when evaluating the design objective.
• seed: optional seed for reproducible design generation.

The returned design has shape:

(l * (p + 1), p)

Computing screening measures

Use softDOE.measure() with a design matrix and response vector:

result = softDOE.measure(design, y)

The response vector y must have one value for each row of the design. The function returns a dictionary with two entries:

• mustar: $\mu^*$ measure of Campolongo et al. (2007).
• t: total Sobol' index of Sobol' (1993).

Testing

After installing the test dependencies, run the test suite with:

python -m pytest

Citation

If you use softDOE, please cite:

@article{yu2026space,
  title={Space-Filling One-Factor-At-A-Time Designs},
  author={Yu, Wei-Yang and Joseph, V Roshan},
  journal={arXiv preprint arXiv:2606.02533},
  year={2026}
}

References

• Sobol', I. M. (1993), "On sensitivity estimation for nonlinear mathematical models," Mathematical Modeling and Computational Experiments, 1, 407-414.
• Campolongo, F., Cariboni, J., and Saltelli, A. (2007), "An effective screening design for sensitivity analysis of large models," Environmental Modelling and Software, 22, 1509-1518.

Authors

Wei-Yang Yu and V. Roshan Joseph.

License

softDOE is licensed under the MIT License.