Package: EnsemblePenReg 0.7

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

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.

Authors:Mansour T.A. Sharabiani, Alireza S. Mahani

EnsemblePenReg_0.7.tar.gz
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EnsemblePenReg.pdf |EnsemblePenReg.html
EnsemblePenReg/json (API)

# Install 'EnsemblePenReg' in R:
install.packages('EnsemblePenReg', repos = c('https://asmahani.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 134 downloads 6 exports 37 dependencies

Last updated 8 years agofrom:8675fc9d99. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winNOTENov 05 2024
R-4.5-linuxNOTENov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:epenregepenreg.baselearner.controlepenreg.integrator.controlepenreg.loadepenreg.saveRegression.Sweep.CV.Fit

Dependencies:bartMachinebartMachineJARsclassclicodetoolscpp11digestdoParalleldoRNGe1071EnsembleBaseforeachgbmglmnetglueigraphiteratorsitertoolskknnlatticelifecyclemagrittrMASSMatrixmissForestnnetpkgconfigproxyrandomForestRcppRcppEigenrJavarlangrngtoolsshapesurvivalvctrs