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Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
| Version: | 0.0.9 |
| Depends: | R (≥ 3.0), methods, grDevices, graphics, stats, MBA, gstat, automap, sp, randomForest |
| Suggests: | MASS, rpart, testthat, DMwR2, ggplot2, e1071 |
| Published: | 2023-10-07 |
| Author: | Paula Branco [aut, cre], Rita Ribeiro [aut, ctb], Luis Torgo [aut, ctb] |
| Maintainer: | Paula Branco <paobranco at gmail.com> |
| BugReports: | https://github.com/paobranco/UBL/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/paobranco/UBL |
| NeedsCompilation: | yes |
| Citation: | UBL citation info |
| CRAN checks: | UBL results |
| Reference manual: | UBL.pdf |
| Package source: | UBL_0.0.9.tar.gz |
| Windows binaries: | r-devel: UBL_0.0.9.zip, r-release: UBL_0.0.9.zip, r-oldrel: UBL_0.0.9.zip |
| macOS binaries: | r-release (arm64): UBL_0.0.9.tgz, r-oldrel (arm64): UBL_0.0.9.tgz, r-release (x86_64): UBL_0.0.9.tgz, r-oldrel (x86_64): UBL_0.0.9.tgz |
| Old sources: | UBL archive |
| Reverse imports: | MSclassifR |
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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.
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