Package: cvwrapr 1.0
cvwrapr: Tools for Cross Validation
Tools for performing cross-validation (CV). The main function is a general purpose wrapper that performs k-fold CV for any tuning parameter in any supervised learning method. The package also has a function that computes the loss incurred by a set of predictions for a variety of loss functions and model families.
Authors:
cvwrapr_1.0.tar.gz
cvwrapr_1.0.zip(r-4.5)cvwrapr_1.0.zip(r-4.4)cvwrapr_1.0.zip(r-4.3)
cvwrapr_1.0.tgz(r-4.4-any)cvwrapr_1.0.tgz(r-4.3-any)
cvwrapr_1.0.tar.gz(r-4.5-noble)cvwrapr_1.0.tar.gz(r-4.4-noble)
cvwrapr_1.0.tgz(r-4.4-emscripten)cvwrapr_1.0.tgz(r-4.3-emscripten)
cvwrapr.pdf |cvwrapr.html✨
cvwrapr/json (API)
| # Install 'cvwrapr' in R: |
| install.packages('cvwrapr', repos = c('https://kjytay.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kjytay/cvwrapr/issues
Last updated 3 years agofrom:9188a9e0b5. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 05 2024 |
| R-4.5-win | OK | Nov 05 2024 |
| R-4.5-linux | OK | Nov 05 2024 |
| R-4.4-win | OK | Nov 05 2024 |
| R-4.4-mac | OK | Nov 05 2024 |
| R-4.3-win | OK | Nov 05 2024 |
| R-4.3-mac | OK | Nov 05 2024 |
Exports:availableTypeMeasurescomputeErrorcoxnet.deviancegetCindexkfoldcv
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Display the names of the measures used in CV for different families | availableTypeMeasures |
| Build a prediction matrix from CV model fits | buildPredMat |
| Check if loss function is valid for a given family | checkValidTypeMeasure |
| Compute CV statistics from a prediction matrix | computeError |
| Compute the nobs by nlambda matrix of errors | computeRawError |
| Compute CV statistics | computeStats |
| Compute deviance for Cox model | coxnet.deviance |
| Compute C index for a Cox model | getCindex |
| Get lambda.min and lambda.1se values | getOptLambda |
| Get full name of loss function | getTypeMeasureName |
| K-fold cross-validation wrapper | kfoldcv |
| Plot the cross-validation curve from a class `cvobj` object | plot.cvobj |
| Print a class `cvobj` object | print.cvobj |
