Package: islasso
Type: Package
Title: The Induced Smoothed Lasso
Version: 1.4.3
Date: 2022-03-01
Author: Gianluca Sottile [aut, cre], 
	Giovanna Cilluffo [aut, ctb], 
	Vito MR Muggeo [aut, cre]
Maintainer: Gianluca Sottile <gianluca.sottile@unipa.it>
Depends: glmnet (>= 4.0), Matrix (>= 1.0-6), R (>= 3.6.0)
Suggests: knitr, lars, xfun, rmarkdown
Description: 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>.
License: GPL (>= 2)
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://journals.sagepub.com/doi/abs/10.1177/0962280219842890
NeedsCompilation: yes
Packaged: 2022-03-01 10:57:45 UTC; gianlucasottile
LazyLoad: yes
Repository: CRAN
Date/Publication: 2022-03-01 15:20:05 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-17 22:19:24 UTC; windows
Archs: i386, x64
