Package: pomodoro 3.8.0
pomodoro: Predictive Power of Linear and Tree Modeling
Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
Authors:
pomodoro_3.8.0.tar.gz
pomodoro_3.8.0.zip(r-4.5)pomodoro_3.8.0.zip(r-4.4)pomodoro_3.8.0.zip(r-4.3)
pomodoro_3.8.0.tgz(r-4.4-any)pomodoro_3.8.0.tgz(r-4.3-any)
pomodoro_3.8.0.tar.gz(r-4.5-noble)pomodoro_3.8.0.tar.gz(r-4.4-noble)
pomodoro_3.8.0.tgz(r-4.4-emscripten)pomodoro_3.8.0.tgz(r-4.3-emscripten)
pomodoro.pdf |pomodoro.html✨
pomodoro/json (API)
| # Install 'pomodoro' in R: |
| install.packages('pomodoro', repos = c('https://seymakalay.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/seymakalay/pomodoro/issues
- sample_data - Sample data for analysis. A dataset containing information of access to credit.
Last updated 3 years agofrom:337b764fb3. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 20 2024 |
| R-4.5-win | NOTE | Nov 20 2024 |
| R-4.5-linux | NOTE | Nov 20 2024 |
| R-4.4-win | NOTE | Nov 20 2024 |
| R-4.4-mac | NOTE | Nov 20 2024 |
| R-4.3-win | OK | Nov 20 2024 |
| R-4.3-mac | OK | Nov 20 2024 |
Exports:BAG_ModelCombined_PerformanceEstimate_ModelsGBM_ModelGLM_ModelMLM_ModelRF_Model
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygbmgenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bagging Model | BAG_Model |
| Combined Performance of the Data Splits | Combined_Performance |
| Results of the Each Data and Data Splits | Estimate_Models |
| Gradient Boosting Model | GBM_Model |
| Generalized Linear Model | GLM_Model |
| Multinominal Logistic Model | MLM_Model |
| Random Forest | RF_Model |
| Sample data for analysis. A dataset containing information of access to credit. | sample_data |
