setqca

A native, typed Python implementation of Qualitative Comparative Analysis (QCA).

setqca is not an R wrapper. It provides an auditable Python implementation of the mathematical core of crisp-set and fuzzy-set QCA, with exact Boolean minimisation and data-science-friendly result objects.

Status: 0.1.0 alpha

Conservative and parsimonious csQCA/fsQCA are the stable focus. Directional intermediate solutions now follow Ragin and Sonnett (2005) and match the reference R QCA implementation on the canonical Lipset datasets.

Design commitments

Commitment	What it means in practice
Exact, not heuristic	Minimisation is classical Quine-McCluskey with a branch-and-bound solution of the prime-implicant chart. All tied minimal covers are returned, not an arbitrary one.
Explicit, not implicit	Every threshold is a named parameter. Ambiguous cases — such as a membership of exactly 0.5 — raise rather than being silently resolved.
Typed end to end	The package ships py.typed and passes mypy --strict.
Honest about maturity	Anything short of parity with R QCA is documented as such rather than quietly approximated.

Quick start

import pandas as pd
from setqca import FSQCA, calibrate_direct

data = pd.DataFrame({"digital": [...], "skills": [...], "innovation": [...]})
for column in data.columns:
    data[column] = calibrate_direct(data[column], full_out=20, crossover=50, full_in=80)

result = FSQCA(consistency=0.85, pri=0.70, frequency=2).fit(
    data, outcome="innovation", conditions=["digital", "skills"]
)
print(result)
print(result.summary_frame("parsimonious"))

Continue with Getting started, or jump to the API reference.

Where to go next

• Getting started — installation and a complete worked analysis.
• Calibration — turning raw measures into set memberships.
• Truth tables — cutoffs, row coding and remainders.
• Minimisation — the exact Boolean engine.
• Methodology — the formal implementation contract.
• Validation — how correctness is established and what is not yet verified.

Citing

If you use setqca in published research, please cite the archived release:

Ribeiro, D. (2026). setqca: Native Python Crisp-Set and Fuzzy-Set Qualitative Comparative Analysis (version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21879360

Cite the version DOI above when the exact version matters for reproducibility; cite the concept DOI 10.5281/zenodo.21879359 to refer to the project as a whole. Citation metadata is provided in CITATION.cff.

License

Released under the MIT License.