industrialstats¶
Industrial statistics and design of experiments for Python.
industrialstats provides reproducible experimental-design generators,
statistical analysis, diagnostics, power calculations, optimization, and
visualization for manufacturing, engineering, and research experiments.
The project is pre-1.0. Its development priority is statistical correctness and validation against established DOE references before the catalogue of design families is widened.
Install¶
Supported Python versions are 3.11 through 3.14.
A first design¶
from industrialstats.designs.base import Factor
from industrialstats.designs.factorial import FactorialDesign
factors = [
Factor("temperature", [180, 220], factor_type="continuous"),
Factor("pressure", [10, 20], factor_type="continuous"),
]
design = FactorialDesign(factors=factors, replicates=2, randomize=True, seed=42)
print(design.generate_design())
Continue with Getting started, or jump to choosing a design.
Project principles¶
- Statistical correctness first. Implementations are validated against textbook results, trusted reference software, or independently derived properties.
- Reproducible experiments. Randomization is seedable and design matrices stay inspectable.
- Transparent methods. Explicit statistical calculations and documented assumptions are preferred over opaque abstractions.
- Clear design semantics. Terms such as effect, block, alias, resolution, whole plot, and optimality criterion carry their precise DOE meanings.
- No false completeness. Partially implemented or statistically provisional methods are labelled as such.
Maturity of each design family¶
| Design family | Status |
|---|---|
| Full factorial | Implemented |
| Fractional factorial | Implemented |
| Completely randomized design | Implemented |
| Randomized complete block design | Implemented |
| Plackett-Burman | Implemented, limited catalogue |
| Definitive screening | Experimental — construction scheduled for correction |
| Response surface methodology | Implemented |
| Optimal designs | Implemented |
| Split-plot | Basic — error-stratum analysis incomplete |
| Mixture | Basic |
See the roadmap for the full sequence.