islasso: The Induced Smoothed Lasso

An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper: Cilluffo, G., Sottile, G., La Grutta, S. and Muggeo, V. (2019) The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. <doi:10.1177/0962280219842890>, and discussed in a tutorial: Sottile, G., Cilluffo, G., and Muggeo, V. (2019) The R package islasso: estimation and hypothesis testing in lasso regression. <doi:10.13140/RG.2.2.16360.11521>.

Version: 1.3.1
Depends: glmnet (≥ 4.0), Matrix (≥ 1.0-6), R (≥ 3.6.0)
Suggests: knitr, lars, xfun, rmarkdown
Published: 2021-06-16
Author: Gianluca Sottile [aut, cre], Giovanna Cilluffo [aut, ctb], Vito MR Muggeo [aut, cre]
Maintainer: Gianluca Sottile <gianluca.sottile at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: islasso citation info
Materials: NEWS
CRAN checks: islasso results


Reference manual: islasso.pdf
Vignettes: An Introduction to islasso
Package source: islasso_1.3.1.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): islasso_1.3.1.tgz, r-release (x86_64): islasso_1.3.1.tgz, r-oldrel: islasso_1.3.1.tgz
Old sources: islasso archive


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