Package: CSMES 1.0.1
CSMES: Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain Cost Conditions
Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.
Authors:
CSMES_1.0.1.tar.gz
CSMES_1.0.1.zip(r-4.5)CSMES_1.0.1.zip(r-4.4)CSMES_1.0.1.zip(r-4.3)
CSMES_1.0.1.tgz(r-4.4-any)CSMES_1.0.1.tgz(r-4.3-any)
CSMES_1.0.1.tar.gz(r-4.5-noble)CSMES_1.0.1.tar.gz(r-4.4-noble)
CSMES_1.0.1.tgz(r-4.4-emscripten)CSMES_1.0.1.tgz(r-4.3-emscripten)
CSMES.pdf |CSMES.html✨
CSMES/json (API)
# Install 'CSMES' in R: |
install.packages('CSMES', repos = c('https://koendebock.r-universe.dev', 'https://cloud.r-project.org')) |
- BFP - Business failure prediction demonstration data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:a2cd47511f. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Aug 25 2024 |
R-4.5-win | OK | Aug 25 2024 |
R-4.5-linux | OK | Aug 25 2024 |
R-4.4-win | OK | Aug 25 2024 |
R-4.4-mac | OK | Aug 25 2024 |
R-4.3-win | OK | Aug 25 2024 |
R-4.3-mac | OK | Aug 25 2024 |
Exports:brierCurveCSMES.ensNomCurveCSMES.ensSelCSMES.predictCSMES.predictParetoplotBrierCurve
Dependencies:bitopscaToolsdata.tablegplotsgtoolsKernSmoothlatticemcoROCRrpartzoo