SWORD: Support Vector Machine Weighted Oblique Random Decision Forests
Implements SWORD (Support vector machine Weighted Oblique Random
Decision forests), a random forest method for regression based on TORS
(Tree Oblique for Regression with weighted SVM) oblique regression trees. At each internal node a
Weighted-SVM classifier with a linear kernel defines the oblique splitting
hyperplane as a sparse linear combination of predictors. The C++
coordinate-descent solver provides fast training. Variable importance is
computed via OIW-VI (Oblique Impurity-Weighted Variable Importance).
Provides tree fitting (TORS), forest fitting (SWORD), prediction, and
variable importance (VI_SWORD). Parallel tree fitting is supported via the
'future'/'furrr' framework.
The SWORD method is described in Carta and Frigau (accepted, in press)
'Oblique Random Forests for Regression via Weighted Support Vector Machine',
Advances in Data Analysis and Classification.
The TORS algorithm is described in Carta and Frigau (2025)
<doi:10.1007/s00180-025-01647-w>. The weighted SVM is based on
Yang, Song and Cao (2005) <doi:10.1109/IJCNN.2005.1555965> and the
'WeightSVM' package by Xu et al. (2024)
<doi:10.32614/CRAN.package.WeightSVM>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
Rcpp (≥ 1.0.0) |
| LinkingTo: |
Rcpp |
| Suggests: |
infotheo, furrr, progressr, future, visNetwork, microbenchmark, knitr, rmarkdown, ggplot2, MASS, testthat (≥
3.0.0) |
| Published: |
2026-10-29 |
| DOI: |
10.32614/CRAN.package.SWORD (may not be active yet) |
| Author: |
Andrea Carta 
[aut, cre],
Luca Frigau [aut] |
| Maintainer: |
Andrea Carta <andrea.carta88 at unica.it> |
| BugReports: |
https://github.com/acarta88/sword-package/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/acarta88/sword-package |
| NeedsCompilation: |
yes |
| Citation: |
SWORD citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
SWORD results |
Documentation:
Downloads:
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