autotune-package: autotune: Lasso with data-driven tuning for linear models

In autotune: Faster and more Efficient Lasso (than 'glmnet' and 'scalreg') with Data-Driven Tuning

autotune: Lasso with data-driven tuning for linear models

Description

This package fits lasso path for high-dimensional regression using coordinate descent while automatically tuning it's regularization parameter. autotune Lasso is 10-50 times than the standard glmnet implementation of lasso, and over 100 times faster than scaled lasso. Additionally, it gives reliable noise level estimate for the regression problem.

Details

Package:	autotune
Type:	Package
Version:	1.0
Date:	2025-09-18

Very simple to use. Accepts x,y data for linear regression model, and produces the regularization path which automatically estimates an optimal tuning parameter lambda.

Functions

Only 1 function, more autotune algorithms for VAR, random forests, coming soon!

• autotune_lasso

Author(s)

Tathagata Sadhukhan Ines Wilms Stephan Smeekes and Sumanta Basu
Maintainer: Tathagata Sadhukhan([e-mail])

Examples

set.seed(10)
n <- 80
p <- 500
s <- 5
type <- 1
snr <- 3
betatrue <- c(rep(1,s), rep(0, p - s))
x <- scale(matrix(rnorm(n * p), ncol = p))
error.sd <- sqrt((betatrue %*% betatrue)/snr)
err <- rnorm(n, sd = error.sd)
y <- x %*% betatrue + err
y <- y - mean(y)
ans <- autotune_lasso(x, y, trace_it = TRUE)
b <- betatrue
# The Predictors which are actually significant:
which(b != 0)
# The Predictors which had nonzero estmated coefficients:
which(ans$beta != 0)
# Top 10 predictors X_i's in the ranking of X_i's given by autotune:
ans$sorted_predictors[1:10] + 1
# No of significant predictors in each CD iteration when sigma_hat is allowed to vary:
ans$count_sig_beta
# Sigma estimates in each CD iteration:
ans$sigma2_seq
# Empirical noise variance:
var(err)

autotune documentation built on Aug. 21, 2026, 5:18 p.m.