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srlars  

Fast and Scalable Cellwise-Robust Ensemble
View on CRAN: Click here


Download and install srlars package within the R console
Install from CRAN:
install.packages("srlars")

Install from Github:
library("remotes")
install_github("cran/srlars")

Install by package version:
library("remotes")
install_version("srlars", "3.1.0")



Attach the package and use:
library("srlars")
Maintained by
Anthony Christidis
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-06-29
Latest Update: 2023-07-17
Description:
Functions to perform split robust least angle regression. The approach first uses the least angle regression algorithm to split the variables into the models of an ensemble and robust estimates of the correlation between predictors. An elastic net estimator is then applied to the selected predictors in each model using the imputed data from the detect deviating cell (DDC) method.
How to cite:
Anthony Christidis (2023). srlars: Fast and Scalable Cellwise-Robust Ensemble. R package version 3.1.0, https://cran.r-project.org/web/packages/srlars. Accessed 30 Oct. 2026.
Previous versions and publish date:
(2026-09-20 04:31), 1.0.0 (2023-06-29 16:50), 1.0.1 (2023-07-17 02:00), 2.0.0 (2026-03-02 07:10), 2.0.1 (2026-03-30 07:40), 3.0.0 (2026-06-12 09:10), 3.0.1 (2026-07-02 04:30)
Other packages that cited srlars R package
View srlars citation profile
Other R packages that srlars depends, imports, suggests or enhances
Complete documentation for srlars
Functions, R codes and Examples using the srlars R package
Some associated functions: coef.srlars . predict.srlars . srlars . 
Some associated R codes: Coefficients.R . DataCheck.R . Predictions.R . add_optimal_predictor.R . find_optimal.R . initialize_ensembles.R . remove_optimal_predictor.R . srlars.R .  Full srlars package functions and examples
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