LOWESS Project
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, Node.js, C++, Go, Java, and WebAssembly.
The lowess-project also offers bindings for Rust, Python, R, Julia, Node.js, WebAssembly, C++, Go, and Java — see the full repository.
Installation & Documentation
Currently available for R, Python, Rust, Julia, Node.js, WebAssembly, and C++. See the Installation Guide for detailed installation instructions.
📚 View the full documentation
GPU Backend
GPU acceleration (wgpu: Vulkan, Metal, DirectX 12, and GLES on Android) is also supported for high-throughput batch smoothing. See the GPU Backend guide for details.
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) |
Read more about how LOWESS works in the Concepts.
Note: For a LOESS implementation, use
loess-project.
Why this package?
Speed
It is on average 200-327x faster than Python's statsmodels.lowess and up to 7.8× faster than base R's stats::lowess on a tested large, wide-window workload. GPU fastLowess first beats CPU-parallel execution at 25K–50K points, depending on smoothing fraction, and reaches 4.7× speedup over parallel fastLowess CPU execution at 1M points in the tested workload. Small workloads can favor CPU because of GPU overhead; results vary by workload and hardware. See the Benchmarks page for details.
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 |
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for more information.
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}
}
1 maintainer
Metadata
1 maintainer
Release files for fastlowess 5.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| fastlowess-5.0.0.tar.gz | 696.2 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| fastlowess-5.0.0-cp38-abi3-win_arm64.whl | CPython 3.8 | abi3 | Windows ARM64 | Details |
| fastlowess-5.0.0-cp38-abi3-win_amd64.whl | CPython 3.8 | abi3 | Windows x86-64 | Details |
| fastlowess-5.0.0-cp38-abi3-musllinux_1_2_x86_64.whl | CPython 3.8 | abi3 | Linux musl 1.2+ x86-64 | Details |
| fastlowess-5.0.0-cp38-abi3-musllinux_1_2_aarch64.whl | CPython 3.8 | abi3 | Linux musl 1.2+ ARM64 | Details |
| fastlowess-5.0.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.8 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| fastlowess-5.0.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.8 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| fastlowess-5.0.0-cp38-abi3-macosx_11_0_arm64.whl | CPython 3.8 | abi3 | macOS 11.0+ ARM64 | Details |
| fastlowess-5.0.0-cp38-abi3-macosx_10_12_x86_64.whl | CPython 3.8 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 5.2 MB
Release files / fastlowess-5.0.0.tar.gz
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