Package: xtune 2.0.0
Jingxuan He
xtune: Regularized Regression with Feature-Specific Penalties Integrating External Information
Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.
Authors:
xtune_2.0.0.tar.gz
xtune_2.0.0.zip(r-4.5)xtune_2.0.0.zip(r-4.4)xtune_2.0.0.zip(r-4.3)
xtune_2.0.0.tgz(r-4.4-any)xtune_2.0.0.tgz(r-4.3-any)
xtune_2.0.0.tar.gz(r-4.5-noble)xtune_2.0.0.tar.gz(r-4.4-noble)
xtune_2.0.0.tgz(r-4.4-emscripten)xtune_2.0.0.tgz(r-4.3-emscripten)
xtune.pdf |xtune.html✨
xtune/json (API)
NEWS
| # Install 'xtune' in R: |
| install.packages('xtune', repos = c('https://jingxuanh.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jingxuanh/xtune/issues
- diet - Simulated diet data to predict weight loss
- example - An simulated example dataset
- example.multiclass - Simulated data with multi-categorical outcome
- gene - Simulated gene data to predict weight loss
Last updated 1 years agofrom:33d88886b6. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | NOTE | Nov 10 2024 |
| R-4.5-linux | NOTE | Nov 10 2024 |
| R-4.4-win | NOTE | Nov 10 2024 |
| R-4.4-mac | NOTE | Nov 10 2024 |
| R-4.3-win | NOTE | Nov 10 2024 |
| R-4.3-mac | NOTE | Nov 10 2024 |
Exports:coef_xtuneestimateVariancemisclassificationmsepredict_xtunextunextune.control
Dependencies:adaptMCMCcodacodetoolscrayonforeachglmnetintervalsiteratorslatticelbfgsMASSMatrixramcmcRcppRcppArmadilloRcppEigenselectiveInferenceshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract model coefficients from fitted 'xtune' object | coef_xtune |
| Simulated diet data to predict weight loss | diet |
| Estimate noise variance given predictor X and continuous outcome Y. | estimateVariance |
| An simulated example dataset | example |
| Simulated data with multi-categorical outcome | example.multiclass |
| Simulated gene data to predict weight loss | gene |
| Calculate misclassification error | misclassification |
| Calculate mean square error | mse |
| Model predictions based on fitted 'xtune' object | predict_xtune |
| Regularized regression incorporating external information | xtune |
| Control function for xtune fitting | xtune.control |