Package: MDFS 1.5.5
MDFS: MultiDimensional Feature Selection
Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.
Authors:
MDFS_1.5.5.tar.gz
MDFS_1.5.5.zip(r-4.7-x86_64)MDFS_1.5.5.zip(r-4.7-arm64)MDFS_1.5.5.zip(r-4.6-x86_64)MDFS_1.5.5.zip(r-4.6-arm64)MDFS_1.5.5.zip(r-4.5-x86_64)
MDFS_1.5.5.tgz(r-4.6-x86_64)MDFS_1.5.5.tgz(r-4.6-arm64)MDFS_1.5.5.tgz(r-4.5-x86_64)MDFS_1.5.5.tgz(r-4.5-arm64)
MDFS_1.5.5.tar.gz(r-4.7-arm64)MDFS_1.5.5.tar.gz(r-4.7-x86_64)MDFS_1.5.5.tar.gz(r-4.6-arm64)MDFS_1.5.5.tar.gz(r-4.6-x86_64)
MDFS_1.5.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MDFS/json (API)
| # Install 'MDFS' in R: |
| install.packages('MDFS', repos = c('https://yoctozepto.r-universe.dev', 'https://cloud.r-project.org')) |
- madelon - An artificial dataset called MADELON
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:13a5904b30. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 132 | ||
| linux-devel-x86_64 | OK | 117 | ||
| source / vignettes | OK | 156 | ||
| linux-release-arm64 | OK | 127 | ||
| linux-release-x86_64 | OK | 109 | ||
| macos-release-arm64 | OK | 96 | ||
| macos-release-x86_64 | OK | 243 | ||
| macos-oldrel-arm64 | OK | 104 | ||
| macos-oldrel-x86_64 | OK | 231 | ||
| windows-devel-arm64 | OK | 209 | ||
| windows-devel-x86_64 | OK | 107 | ||
| windows-release-arm64 | OK | 194 | ||
| windows-release-x86_64 | OK | 79 | ||
| windows-oldrel-x86_64 | OK | 79 | ||
| wasm-release | OK | 89 |
Exports:AddContrastVariablesComputeInterestingTuplesComputeInterestingTuplesDiscreteComputeMaxInfoGainsComputeMaxInfoGainsDiscreteComputePValueDiscretizeGenContrastVariablesGetRangeMDFSmdfs_omp_set_num_threadsRelevantVariables
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add contrast variables to data | AddContrastVariables |
| as.data.frame S3 method implementation for MDFS | as.data.frame.MDFS |
| Interesting tuples | ComputeInterestingTuples |
| Interesting tuples (discrete) | ComputeInterestingTuplesDiscrete |
| Max information gains | ComputeMaxInfoGains |
| Max information gains (discrete) | ComputeMaxInfoGainsDiscrete |
| Compute p-values from information gains and return MDFS | ComputePValue |
| Discretize variable on demand | Discretize |
| Generate contrast variables from data | GenContrastVariables |
| Get the recommended range for multiple discretisations | GetRange |
| An artificial dataset called MADELON | madelon |
| Run end-to-end MDFS | MDFS |
| Call omp_set_num_threads | mdfs_omp_set_num_threads |
| Plot MDFS details | plot.MDFS |
| Find indices of relevant variables | RelevantVariables |
| Find indices of relevant variables from MDFS | RelevantVariables.MDFS |