Package: weightederm 0.1.0
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>.
Authors:
weightederm_0.1.0.tar.gz
weightederm_0.1.0.zip(r-4.7)weightederm_0.1.0.zip(r-4.6)weightederm_0.1.0.zip(r-4.5)
weightederm_0.1.0.tgz(r-4.6-any)weightederm_0.1.0.tgz(r-4.5-any)
weightederm_0.1.0.tar.gz(r-4.7-any)weightederm_0.1.0.tar.gz(r-4.6-any)
weightederm_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
weightederm/json (API)
NEWS
| # Install 'weightederm' in R: |
| install.packages('weightederm', repos = c('https://gabrielarpino.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gabrielarpino/weightederm-r/issues
Last updated from:1c90156fe5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 129 | ||
| source / vignettes | OK | 152 | ||
| linux-release-x86_64 | OK | 142 | ||
| macos-release-arm64 | OK | 149 | ||
| macos-oldrel-arm64 | OK | 210 | ||
| windows-devel | OK | 152 | ||
| windows-release | OK | 174 | ||
| windows-oldrel | OK | 131 | ||
| wasm-release | OK | 98 |
Exports:weightederm_configure_pythonwerm_huberwerm_huber_cvwerm_least_squareswerm_least_squares_cvwerm_logisticwerm_logistic_cv
Dependencies:herejsonlitelatticeMatrixpngrappdirsRcppRcppTOMLreticulaterlangrprojrootwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract last-segment coefficients | coef.werm_fit |
| Predict using the last-segment refit | predict.werm_fit |
| Summarise a fitted WERM model | summary.werm_fit |
| Configure which Python environment weightederm uses | weightederm_configure_python |
| Fit a WERM changepoint model with Huber loss | werm_huber |
| Fit a WERM changepoint model with Huber loss and CV selection | werm_huber_cv |
| Fit a WERM changepoint model with squared loss | werm_least_squares |
| Fit a WERM changepoint model with squared loss and CV selection | werm_least_squares_cv |
| Fit a WERM changepoint model with binary logistic loss | werm_logistic |
| Fit a WERM changepoint model with logistic loss and CV selection | werm_logistic_cv |
