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plasso
View on CRAN: Click
here
Download and install plasso package within the R console
Install from CRAN:
install.packages("plasso")
Install from Github:
library("remotes")
install_github("cran/plasso") Install by package version:
library("remotes")
install_version("plasso", "0.1.3") Attach the package and use:
library("plasso")
Maintained by
Glaisner Stefan
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-08-21
Latest Update:
Description:
Built on top of the 'glmnet' library by Friedman, Hastie and Tibshirani (2010) , the 'plasso' package follows Knaus (2022) and comes up with two functions that estimate least squares Lasso and Post-Lasso models.
The plasso() function adds coefficient paths for a Post-Lasso model to the standard 'glmnet' output.
On top of that cv.plasso() cross-validates the coefficient paths for both the Lasso and Post-Lasso model and provides optimal hyperparameter values for the penalty term lambda.
How to cite:
Glaisner Stefan (2023). plasso: Cross-Validated Post-Lasso. R package version 0.1.3, https://cran.r-project.org/web/packages/plasso. Accessed 28 Jun. 2026.
Previous versions and publish date:
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Complete documentation for plasso
Functions, R codes and Examples using
the plasso R package
Some associated functions: CV_core . add_intercept . coef.cv.plasso . coef.plasso . cv.plasso . find_Xse_ind . fit_betas . fitted_values_cv . handle_weights . norm_w_to_n . plasso . plot.cv.plasso . plot.plasso . predict.cv.plasso . predict.plasso . print.cv.plasso . print.plasso . print.summary.cv.plasso . summary.cv.plasso . summary.plasso . toeplitz .
Some associated R codes: cv.plasso.R . internal.plasso.R . plasso.R . toeplitz.R . Full plasso package functions and examples
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