LOWESS Project

PyPI fastlowess (Python) CI

One LOWESS to Rule Them All
One LOWESS to Rule Them All

The fastest, most robust, and most feature-complete language-agnostic LOWESS (Locally Weighted Scatterplot Smoothing) implementation for Rust, Python, R, Julia, JavaScript, C++, and WebAssembly.

The lowess-project also offers bindings for Rust, Python, R, Julia, Node.js, WebAssembly, and C++ — see the full repository.

Installation

[!NOTE]

Currently available for R, Python, Rust, Julia, Node.js, WebAssembly, and C++. See the Installation Guide for detailed installation instructions.

GPU Backend

In addition to parallel = true (multi-core CPU), the batch Lowess class in every binding — except WebAssembly — as well as the fastLowess Rust crate itself, can run on the GPU via wgpu (Vulkan/Metal/DX12). It's opt-in and worth enabling for high-throughput processing of large datasets (roughly 10k+ points); for smaller inputs the CPU backend is typically faster. StreamingLowess/OnlineLowess remain CPU-only. See the GPU Backend guide for installation instructions and usage.

Documentation

[!NOTE]

📚 View the full documentation

LOESS vs. LOWESS

Feature LOESS LOWESS (This Crate)
Polynomial Degree Linear, Quadratic, Cubic, Quartic Linear (Degree 1)
Dimensions Multivariate (n-D support) Univariate (1-D only)
Flexibility High (Distance metrics) Standard
Complexity Higher (Matrix inversion) Lower (Weighted average/slope)

[!TIP] Note: For a LOESS implementation, use loess-project.

Why this package?

Speed

The lowess project beats the competition in terms of speed, whether in single-threaded or multi-threaded parallel execution. It is on average 200-327x faster than Python's statsmodels.lowess and 2-3x faster than R's lowess.

For more details on the performance comparison, see the Benchmarks page.

Robustness

This implementation is more robust than R's lowess and Python's statsmodels due to two key design choices:

MAD-Based Scale Estimation:

For robustness weight calculations, this crate uses Median Absolute Deviation (MAD) for scale estimation:

s = median(|r_i - median(r)|)

In contrast, statsmodels and R's lowess uses the median of absolute residuals (MAR):

s = median(|r_i|)
  • MAD is a breakdown-point-optimal estimator—it remains valid even when up to 50% of data are outliers.
  • The median-centering step removes asymmetric bias from residual distributions.
  • MAD provides consistent outlier detection regardless of whether residuals are centered around zero.

Boundary Padding:

This crate applies a range of different boundary policies at dataset edges:

  • Extend: Repeats edge values to maintain local neighborhood size.
  • Reflect: Mirrors data symmetrically around boundaries.
  • Zero: Pads with zeros (useful for signal processing).
  • NoBoundary: Original Cleveland behavior

statsmodels and R's lowess do not apply boundary padding, which can lead to:

  • Biased estimates near boundaries due to asymmetric local neighborhoods.
  • Increased variance at the edges of the smoothed curve.

Features

A variety of features, supporting a range of use cases:

Feature This package statsmodels R (stats)
Kernel 7 options only Tricube only Tricube
Robustness Weighting 3 options only Huber only Huber
Scale Estimation 2 options only MAR only MAR
Boundary Padding 4 options no padding no padding
Zero Weight Fallback 3 options no no
Auto Convergence yes no no
Online Mode yes no no
Streaming Mode yes no no
Confidence Intervals yes no no
Prediction Intervals yes no no
Cross-Validation 2 options no no
Parallel Execution yes no no
GPU Acceleration yes no no
no-std Support yes no no

Validation

All implementations are numerical twins of R's lowess:

Aspect Status Details
Accuracy ✅ EXACT MATCH Max diff < 1e-12 across all scenarios
Consistency ✅ PERFECT Multiple scenarios pass with strict tolerance
Robustness ✅ VERIFIED Robust smoothing matches R exactly

API Reference

from fastlowess import Lowess

model = Lowess(
    fraction=0.5,
    iterations=3,
    delta=0.01,
    weight_function="tricube",
    robustness_method="bisquare",
    scaling_method="mad",
    zero_weight_fallback="use_local_mean",
    boundary_policy="extend",
    confidence_intervals=0.95,
    prediction_intervals=0.95,
    return_diagnostics=True,
    return_residuals=True,
    return_robustness_weights=True,
    return_se=True,
    cv_fractions=[0.3, 0.5, 0.7],
    cv_method="kfold",
    cv_k=5,
    cv_seed=123,
    auto_converge=1e-4,
    parallel=True
)
custom_weights = [1.0] * len(x)
result = model.fit(x, y, custom_weights=custom_weights)

# Result structure:
result.x,
result.y,
result.standard_errors,
result.confidence_lower,
result.confidence_upper,
result.prediction_lower,
result.prediction_upper,
result.residuals,
result.robustness_weights,
result.diagnostics,
result.iterations_used,
result.fraction_used,
result.cv_scores

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for more information.

Changelog

See CHANGELOG.md for a history of changes.

License

Licensed under MIT or Apache-2.0.

Citation

If you use this software in your research, please cite it using the CITATION.cff file or the BibTeX entry below:

@software{lowess_project,
  author = {Valizadeh, Amir},
  title = {LOWESS Project: High-Performance Locally Weighted Scatterplot Smoothing},
  year = {2026},
  url = {https://github.com/thisisamirv/lowess-project},
  license = {MIT OR Apache-2.0}
}

Project links

Data verified by PyPI on Aug 25, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (4.0.0)

1 maintainer

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Avatar for thisisamirv from gravatar.com thisisamirv

Credits

Author: Amir Valizadeh

GitHub Statistics

Data verified by PyPI on Aug 25, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT OR Apache-2.0
View SPDX License List

Requires

Python >=3.8

Tags

lowess smoothing statistics regression numpy

Classifiers

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