Package: ipred 0.9-15
ipred: Improved Predictors
Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.
Authors:
ipred_0.9-15.tar.gz
ipred_0.9-15.zip(r-4.5)ipred_0.9-15.zip(r-4.4)ipred_0.9-15.zip(r-4.3)
ipred_0.9-15.tgz(r-4.4-x86_64)ipred_0.9-15.tgz(r-4.4-arm64)ipred_0.9-15.tgz(r-4.3-x86_64)ipred_0.9-15.tgz(r-4.3-arm64)
ipred_0.9-15.tar.gz(r-4.5-noble)ipred_0.9-15.tar.gz(r-4.4-noble)
ipred_0.9-15.tgz(r-4.4-emscripten)ipred_0.9-15.tgz(r-4.3-emscripten)
ipred.pdf |ipred.html✨
ipred/json (API)
NEWS
| # Install 'ipred' in R: |
| install.packages('ipred', repos = c('https://thothorn.r-universe.dev', 'https://cloud.r-project.org')) |
- DLBCL - Diffuse Large B-Cell Lymphoma
- GlaucomaMVF - Glaucoma Database
- Smoking - Smoking Styles
- dystrophy - Detection of muscular dystrophy carriers.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:f93fd84a1d. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 16 2024 |
| R-4.5-win-x86_64 | OK | Sep 16 2024 |
| R-4.5-linux-x86_64 | OK | Sep 16 2024 |
| R-4.4-win-x86_64 | OK | Sep 16 2024 |
| R-4.4-mac-x86_64 | OK | Sep 16 2024 |
| R-4.4-mac-aarch64 | OK | Sep 16 2024 |
| R-4.3-win-x86_64 | OK | Sep 16 2024 |
| R-4.3-mac-x86_64 | OK | Sep 16 2024 |
| R-4.3-mac-aarch64 | OK | Sep 16 2024 |
Exports:baggingbootestcontrol.errorestcverrorestgetsurvinbagginclassipredbaggipredknnkfoldcvmypredict.lmpredict.ipredknnrsurvsbriersldavarset
Dependencies:classclicodetoolsdata.tablediagramdigestfuturefuture.applyglobalsKernSmoothlatticelavalistenvMASSMatrixnnetnumDerivparallellyprodlimprogressrRcpprpartshapeSQUAREMsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bagging Classification, Regression and Survival Trees | bagging bagging.data.frame bagging.default ipredbagg ipredbagg.default ipredbagg.factor ipredbagg.integer ipredbagg.numeric ipredbagg.Surv |
| Bootstrap Error Rate Estimators | bootest bootest.default bootest.factor bootest.integer bootest.numeric bootest.Surv |
| Control Error Rate Estimators | control.errorest |
| Cross-validated Error Rate Estimators. | cv cv.default cv.factor cv.integer cv.numeric cv.Surv |
| Diffuse Large B-Cell Lymphoma | DLBCL |
| Detection of muscular dystrophy carriers. | dystrophy |
| Estimators of Prediction Error | errorest errorest.data.frame errorest.default |
| Glaucoma Database | GlaucomaMVF |
| Indirect Bagging | inbagg inbagg.data.frame inbagg.default |
| Indirect Classification | inclass inclass.data.frame inclass.default |
| k-Nearest Neighbour Classification | ipredknn |
| Subsamples for k-fold Cross-Validation | kfoldcv |
| Predictions Based on Linear Models | mypredict.lm |
| Predictions from Bagging Trees | predict.classbagg predict.regbagg predict.survbagg |
| Predictions from an Inbagg Object | predict.inbagg |
| Predictions from an Inclass Object | predict.inclass |
| Predictions from k-Nearest Neighbors | predict.ipredknn |
| Predictions from Stabilised Linear Discriminant Analysis | predict.slda |
| Print Method for Bagging Trees | print print.classbagg print.regbagg print.survbagg |
| Print Method for Error Rate Estimators | print.bootestclass print.bootestreg print.bootestsurv print.cvclass print.cvreg print.cvsurv |
| Print Method for Inbagg Object | print.inbagg |
| Print Method for Inclass Object | print.inclass |
| Pruning for Bagging | prune.classbagg prune.regbagg prune.survbagg |
| Simulate Survival Data | rsurv |
| Model Fit for Survival Data | sbrier |
| Stabilised Linear Discriminant Analysis | slda slda.default slda.factor slda.formula |
| Smoking Styles | Smoking |
| Summarising Bagging | print.summary.bagging summary.classbagg summary.regbagg summary.survbagg |
| Summarising Inbagg | print.summary.inbagg summary.inbagg |
| Summarising Inclass | print.summary.inclass summary.inclass |
| Simulation Model | varset |
