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A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | adabag, base, C50, caret, caTools, data.table, doParallel, dplyr, e1071, earth, evtree, frbs, glmnet, gmodels, hda, HDclassif, ipred, kernlab, kknn, klaR, magrittr, MASS, Matrix, mda, MLmetrics, nnet, parallel, party, pls, randomForest, rpartScore, sparseLDA, stats, themis, utils |
| Suggests: | h2o |
| Published: | 2023-05-17 |
| Author: | Jay Devine [aut, cre, cph], Bened'ikt Hallgrimsson [aut] |
| Maintainer: | Jay Devine <jay.devine1 at ucalgary.ca> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Citation: | pheble citation info |
| Materials: | README NEWS |
| CRAN checks: | pheble results |
| Reference manual: | pheble.pdf |
| Package source: | pheble_0.1.0.tar.gz |
| Windows binaries: | r-devel: pheble_0.1.0.zip, r-release: pheble_0.1.0.zip, r-oldrel: pheble_0.1.0.zip |
| macOS binaries: | r-release (arm64): pheble_0.1.0.tgz, r-oldrel (arm64): pheble_0.1.0.tgz, r-release (x86_64): pheble_0.1.0.tgz, r-oldrel (x86_64): pheble_0.1.0.tgz |
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