Package: corkscrew 1.1
corkscrew: Preprocessor for Data Modeling
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables using data transformations.
Authors:
corkscrew_1.1.tar.gz
corkscrew_1.1.zip(r-4.7)corkscrew_1.1.zip(r-4.6)corkscrew_1.1.zip(r-4.5)
corkscrew_1.1.tgz(r-4.6-any)corkscrew_1.1.tgz(r-4.5-any)
corkscrew_1.1.tar.gz(r-4.7-any)corkscrew_1.1.tar.gz(r-4.6-any)
corkscrew_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
corkscrew/json (API)
| # Install 'corkscrew' in R: |
| install.packages('corkscrew', repos = c('https://sasan-puri.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b13e370dc1. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 127 | ||
| source / vignettes | OK | 209 | ||
| linux-release-x86_64 | OK | 127 | ||
| macos-release-arm64 | OK | 158 | ||
| macos-oldrel-arm64 | OK | 156 | ||
| windows-devel | OK | 135 | ||
| windows-release | OK | 126 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 118 |
Exports:apply.ctocapply.tbinctoctbintransformation
Dependencies:bitopscaToolsclicpp11farverggplot2gluegplotsgtablegtoolsigraphisobandKernSmoothlabelinglatticelifecyclemagrittrMatrixpkgconfigR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Preprocessor for Data Modeling | corkscrew-package corkscrew |
| Applying Categorical to Continuous conversion to a new dataframe | apply.ctoc |
| Extrapolate t-test based binning to a new data | apply.tbin |
| Categorical variables into Continuous features | ctoc |
| t-test based binning | tbin |
| Relationship between the response variables and predictors | transformation |
