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:Koen W. De Bock, Kristof Coussement and Stefan Lessmann

CSMES_1.0.1.tar.gz
CSMES_1.0.1.zip(r-4.7)CSMES_1.0.1.zip(r-4.6)CSMES_1.0.1.zip(r-4.5)
CSMES_1.0.1.tgz(r-4.6-any)CSMES_1.0.1.tgz(r-4.5-any)
CSMES_1.0.1.tar.gz(r-4.7-any)CSMES_1.0.1.tar.gz(r-4.6-any)
CSMES_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CSMES/json (API)

# Install 'CSMES' in R:
install.packages('CSMES', repos = c('https://koendebock.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • BFP - Business failure prediction demonstration data set

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 280 downloads 6 exports 11 dependencies

Last updated from:a2cd47511f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK159
source / vignettesOK158
linux-release-x86_64OK155
macos-release-arm64OK85
macos-oldrel-arm64OK79
windows-develOK68
windows-releaseOK77
windows-oldrelOK76
wasm-releaseOK95

Exports:brierCurveCSMES.ensNomCurveCSMES.ensSelCSMES.predictCSMES.predictParetoplotBrierCurve

Dependencies:bitopscaToolsdata.tablegplotsgtoolsKernSmoothlatticemcoROCRrpartzoo