pylmrob 0.5.22

Python port of robustbase::lmrob (MM regression)

pylmrob

Python port of the lmrob MM-estimator from R's robustbase package.

Coefficient agreement with R on the classical validation datasets is within rtol=1e-3 for both estimates and covariance; see docs/numerical-notes.md for known divergences and docs/bench-report.md for wall-clock numbers.

Status: alpha. Public API is provisional.

Note: parts of the documentation were drafted with help from Claude Opus 4.7. Verify against the source code or the cited papers before relying on them.

Install

pip install pylmrob

From source (requires Python 3.10+ and a C compiler):

git clone https://github.com/anevolbap/pyrobustlm
cd pyrobustlm
uv pip install --no-build-isolation -e ".[dev]"
pytest

Quickstart

import pandas as pd
from pylmrob import lmrob, Control

df = pd.read_csv("stackloss.csv")
fit = lmrob(
    "stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.",
    df,
    control=Control(setting="KS2014"),
    seed=42,
)
print(fit.summary())

More: docs/quickstart.md, docs/examples/, docs/porting-from-r.md.

Comparison with statsmodels

statsmodels.robust.RLM implements M-estimators (Huber, Tukey, Hampel) but not MM-estimators. If you need MM (high breakdown + high efficiency), use pylmrob. If plain M is enough, RLM has a more mature API.

Contributing

See CONTRIBUTING.md for the dev setup, test layout, and PR guidelines.

References

• Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2). doi:10.1214/aos/1176350366.
• Salibian-Barrera, M. and Yohai, V. J. (2006). A Fast Algorithm for S-Regression Estimates. JCGS, 15(2). doi:10.1198/106186006X113629.
• Koller, M. and Stahel, W. A. (2011). Sharpening Wald-type Inference in Robust Regression for Small Samples. CSDA, 55(8). doi:10.1016/j.csda.2011.02.014.
• Koller, M. and Stahel, W. A. (2017). Nonsingular Subsampling for Regression S-estimators with Categorical Predictors. Computational Statistics, 32(2). doi:10.1007/s00180-016-0679-x.
• Maronna, R. A. and Yohai, V. J. (2000). Robust regression with both continuous and categorical predictors. JSPI, 89(1-2). doi:10.1016/S0378-3758(99)00208-6.

License and citation

GPL-3.0-or-later, matching robustbase. See LICENSE and NOTICE. Citation metadata in CITATION.cff (GitHub renders a "Cite this repository" button).