Description
The Induced Smoothed Lasso.
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; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
README.md
Induced Smoothed Lasso
📦 Installation
You can install the development version of islasso from GitHub:
# install.packages("devtools")
devtools::install_github("gianluca-sottile/islasso")
Once installed, load the package:
library(islasso)
🔍 Description
islasso implements the Induced Smoothed Lasso, a robust and interpretable approach for hypothesis testing in high-dimensional linear and generalized linear models.
Key features include:
- Efficient Fortran backend for fast computation
- Support for Gaussian, Binomial, Poisson, and Gamma families
- Smoothed penalization for stable inference
- Automatic selection of active variables
- Visualization tools powered by
ggplot2
🚀 Quick Example
set.seed(123)
sim <- simulXy(n = 100, p = 20, family = "gaussian")
mod <- islasso(y ~ ., data = sim$data)
summary(mod)
plot(mod)
📚 Documentation
- 📘 Function reference:
?islasso - 📄 Vignette:
vignette("islasso-intro") - 🌐 Website: https://gianluca-sottile.github.io/islasso/
📖 References
Cilluffo G, Sottile G, La Grutta S, Muggeo V (2020). The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. Statistical Methods in Medical Research_, 29(3), 765-777. doi:10.1177/0962280219842890
🤝 Contributing
Feel free to open issues, suggest improvements, or submit pull requests.
Bug reports and feature requests are welcome!
📜 License
islasso © 2019 by Gianluca Sottile is licensed under CC BY 4.0
