Package: EnsemblePCReg 1.1.4

EnsemblePCReg: Extensible Package for Principal-Component-Regression-Based Heterogeneous Ensemble Meta-Learning

Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components 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

EnsemblePCReg_1.1.4.tar.gz
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EnsemblePCReg.pdf |EnsemblePCReg.html✨
EnsemblePCReg/json (API)

# Install 'EnsemblePCReg' in R:
install.packages('EnsemblePCReg', 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.

2.00 score 203 downloads 6 exports 37 dependencies

Last updated 3 years agofrom:6bcd66eec4. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:epcregepcreg.baselearner.controlepcreg.integrator.controlepcreg.loadepcreg.saveRegression.Sweep.CV.Fit

Dependencies:bartMachinebartMachineJARsclassclicodetoolscpp11digestdoParalleldoRNGe1071EnsembleBaseforeachgbmglmnetglueigraphiteratorsitertoolskknnlatticelifecyclemagrittrMASSMatrixmissForestnnetpkgconfigproxyrandomForestRcppRcppEigenrJavarlangrngtoolsshapesurvivalvctrs

Multi-stage heterogeneous ensemble meta-learning with hands-off user-interface and on-demand prediction using principal components regression: The R package EnsemblePCReg

Rendered fromEnsemblePCReg.pdf.asisusingR.rsp::asison Oct 27 2024.

Last update: 2016-06-29
Started: 2016-02-13