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weightederm: Weighted Empirical Risk Minimization for Changepoint Regression

R interface to the 'weightederm' package for 'Python', which provides 'scikit-learn'-style estimators for offline change point regression (data segmentation) via weighted empirical risk minimization. Supports least-squares, Huber, and logistic losses with fixed or cross-validated numbers of change points. Wraps 'Python' via 'reticulate'. Arpino and Venkataramanan (2026) <doi:10.48550/arXiv.2604.11746>.

Version: 0.1.0
Imports: reticulate (≥ 1.28)
Suggests: testthat (≥ 3.0.0)
Published: 2026-04-22
DOI: 10.32614/CRAN.package.weightederm
Author: Gabriel Arpino [aut, cre]
Maintainer: Gabriel Arpino <arpino.gabriel at gmail.com>
BugReports: https://github.com/gabrielarpino/weightederm-r/issues
License: Apache License (≥ 2.0)
URL: https://github.com/gabrielarpino/weightederm-r
NeedsCompilation: no
SystemRequirements: Python (>= 3.9), weightederm Python package
Language: en-US
Materials: README, NEWS
CRAN checks: weightederm results

Documentation:

Reference manual: weightederm.html , weightederm.pdf

Downloads:

Package source: weightederm_0.1.0.tar.gz
Windows binaries: r-release: weightederm_0.1.0.zip, r-oldrel: weightederm_0.1.0.zip
macOS binaries: r-release (arm64): weightederm_0.1.0.tgz, r-oldrel (arm64): weightederm_0.1.0.tgz, r-release (x86_64): weightederm_0.1.0.tgz, r-oldrel (x86_64): weightederm_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.