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plasso (version 0.1.3)

Cross-Validated Post-Lasso

Description

Provides tools for cross-validated Lasso and Post-Lasso estimation. Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) , the main function plasso() extends the standard 'glmnet' output with coefficient paths for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) .

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Install

install.packages('plasso')

Monthly Downloads

185

Version

0.1.3

License

GPL-3

Issues

0

Pull Requests

0

Stars

0

Forks

0

Maintainer

Knaus Michael C.

Last Published

October 31st, 2025

Functions in plasso (0.1.3)

print.cv.plasso

Print cross-validated (Post-) Lasso model
predict.plasso

Predict for (Post-) Lasso models
plasso

Lasso and Post-Lasso
summary.cv.plasso

Summary of cross-validated (Post-) Lasso model
summary.plasso

Summary of (Post-) Lasso model
toeplitz

Simulated 'Toeplitz' Data
plot.plasso

Plot coefficient paths
predict.cv.plasso

Predict after cross-validated (Post-) Lasso
plot.cv.plasso

Plot of cross-validation curves
handle_weights

Sanitizes potential sample weights
coef.cv.plasso

Extract coefficients from a cv.plasso object
cv.plasso

Cross-Validated Lasso and Post-Lasso
coef.plasso

Extract coefficients from a plasso object
print.summary.cv.plasso

Print summary of (Post-) Lasso model
print.plasso

Print (Post-) Lasso model
find_Xse_ind

Helper function to find the position for prespecified SE rules
fitted_values_cv

Fitted values for a subset of active variables
fit_betas

plasso fitting
norm_w_to_n

Normalization of sample weights for potential sample weights
add_intercept

Adds an intercept to a matrix
CV_core

Core part for (Post-) Lasso cross-validation