README.md

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

autotune

Repository for R package of autotune Lasso

Installation

You can install the development version from GitHub:

# Install the devtools or remotes package if you don't have it
# install.packages("devtools")

# Ensure that you have the Rcpp package installed with version >=1.0.13
devtools::install_github("Tathagata-S/autotune")

When installing from GitHub, in order to build the package from source, you need to have the appropriate R development tools installed (Rtools on Windows, or these tools on Mac).

Installation with vignette

You can also the build the vignette while installing the development version from GitHub:

# install.packages("devtools")

devtools::install_github("Tathagata-S/autotune", build_vignettes = TRUE, dependencies = TRUE)

vignette("autotune-lasso-vignette")

👉 Usage:

Here’s a quick example:

library(autotune)
?autotune_lasso

set.seed(10)
n <- 80
p <- 400
s <- 5
snr <- 4
betatrue <- c(rep(1,s), rep(0, p - s))
x <- 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 = T)

b <- betatrue
# The Predictors which are actually significant:
which(b != 0)
# The Predictors which had nonzero estimated coefficients:
which(ans$beta != 0)
# Top 10 predictors X_i's in the ranking of X_i's given by autotune:
ans$CD.path.details$sorted_predictors[1:10]
# No of significant predictors in each CD iteration when sigma_hat is allowed to vary:
ans$CD.path.details$count_sig_beta
# Sigma estimates in each CD iteration:
ans$CD.path.details$sigma_sq_seq
# Empirical noise variance:
var(err)

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