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EnsemblePenReg (version 0.8)

Extensible Classes and Methods for Penalized-Regression-Based Integration of Base Learners

Description

Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.

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Version

Install

install.packages('EnsemblePenReg')

Monthly Downloads

32

Version

0.8

License

GPL (>= 2)

Maintainer

Alireza Mahani

Last Published

March 29th, 2025

Functions in EnsemblePenReg (0.8)

Regression.Integrator.PenReg.SelMin.Config-class

Class "Regression.Integrator.PenReg.SelMin.Config"
Regression.Sweep.CV.Fit

Function for cross-validation based sweep operation.
epenreg.save

Custom Functions for Disk I/O in EnsemblePenReg Package
plot.epenreg

Plot function for epenreg model
ecpreg

Penalized-Regression-Based (PenReg) Integration of Base Learners for Ensemble Learning
epenreg.baselearner.control

Utility Functions for Configuring Regression Base Learners and Integrator in EnsemblePenReg Package
predict.epenreg

Predict method for class "epenreg"
Regression.Integrator.PenReg.SelMin.FitObj-class

Class "Regression.Integrator.PenReg.SelMin.FitObj"
Regression.Sweep.PenReg.FitObj-class

Class "Regression.Sweep.PenReg.FitObj"
Regression.Sweep.PenReg.Config-class

Class "Regression.Sweep.PenReg.Config"
Regression.Sweep.CV.FitObj-class

Class "Regression.Sweep.CV.FitObj"