r2py_kernsmooth
A Python port of the R package KernSmooth (v. 2.23-26), which implements the kernel-smoothing methods described in Wand, M.P. and Jones, M.C. (1995), Kernel Smoothing, Chapman and Hall.
The package binds the original KernSmooth Fortran routines directly via f2py, and reimplements the R-level driver logic (argument handling, FFT-based convolution, plug-in bandwidth selection) in Python on top of NumPy and SciPy, so results match the R package to numerical precision.
Installation
pip install r2py_kernsmooth
Building from source requires a Fortran compiler and a BLAS implementation (e.g. OpenBLAS); see meson.build.
Citation
If you use r2py_kernsmooth in published work, please cite all three of
the following. The numerical core of r2py_kernsmooth is KernSmooth's own
Fortran code, retained unmodified — the results you obtain are produced by
Wand's implementation of the methods in Wand & Jones (1995).
-
Wand, M. (2025). KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995). R package version 2.23-26. CRAN. https://cran.r-project.org/package=KernSmooth (the original package, whose Fortran routines this port embeds)
-
Cai, Y. and Li, J. r2py: AI-Assisted Conversion of R Statistical Packages to Python. (in preparation) (the conversion method, and the validation evidence for this port)
-
Cai, Y. and Li, J. (2026). r2py_kernsmooth (version 0.1.1) [Computer software]. PyPI. https://pypi.org/project/r2py_kernsmooth/ (the exact artifact executed — please cite the version you actually ran)
For the underlying statistical methods, please also cite Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman & Hall, London. ISBN 9780412552700.
BibTeX
@Manual{wand2025kernsmooth,
title = {KernSmooth: Functions for Kernel Smoothing Supporting
Wand \& Jones (1995)},
author = {Matt Wand},
year = {2025},
note = {R package version 2.23-26},
url = {https://CRAN.R-project.org/package=KernSmooth}
}
@Article{cai2026r2py,
title = {r2py: AI-Assisted Conversion of R Statistical Packages to Python},
author = {Cai, Yufei and Li, Jun},
year = {2026},
note = {In preparation}
}
@Misc{cai2026r2pykernsmooth,
title = {r2py\_kernsmooth},
author = {Cai, Yufei and Li, Jun},
year = {2026},
note = {Python package version 0.1.1},
url = {https://pypi.org/project/r2py_kernsmooth/}
}
@Book{wandjones1995,
title = {Kernel Smoothing},
author = {Wand, M.~P. and Jones, M.~C.},
publisher = {Chapman \& Hall},
address = {London},
year = {1995},
isbn = {9780412552700}
}
Functions
| Function | Description |
|---|---|
bkde |
Binned kernel density estimate |
bkde2D |
2D binned kernel density estimate |
bkfe |
Binned kernel functional estimate |
dpih |
Direct plug-in histogram bin width selection |
dpik |
Direct plug-in bandwidth selection for kernel density estimation |
dpill |
Direct plug-in bandwidth selection for local linear regression |
locpoly |
Local polynomial regression / density derivative estimation |
linbin / linbin2D |
Linear binning of (1D / 2D) data onto a grid |
rlbin |
Linear binning of regression data |
blkest, cpblock, sdiag, sstdiag |
Internal estimation helpers used by dpill |
Example
import numpy as np
from r2py_kernsmooth import bkde, locpoly
x = np.random.normal(size=1000)
density = bkde(x) # {"x": grid points, "y": density estimate}
y = x + np.random.normal(scale=0.5, size=1000)
fit = locpoly(x, y, bandwidth=0.3) # {"x": grid points, "y": fitted curve}
License
r2py_kernsmooth is distributed under the same "Unlimited" license as upstream KernSmooth: it may be used, copied, modified, and redistributed for any purpose without restriction. See LICENSE for the full text.
Attribution
r2py_kernsmooth is a derivative of the R package KernSmooth, originally authored by Matt Wand, with LINPACK Fortran routines contributed by Cleve Moler and the R-language port maintained by Brian Ripley. The Python port is authored by Yufei Cai (ycai9@nd.edu) and Jun Li (jun.li@nd.edu). See NOTICE for full attribution details.
1 maintainer
Release files for r2py-kernsmooth 0.1.1
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