The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Computational Framework for L\(_{2}\)E Structured Regression Problems

The L2E package (version 2.0) implements the computational framework for L\(_2\)E regression in Liu, Chi, and Lange (2022+), which was built on the previous work in Chi and Chi (2022). Both works employ the block coordinate descent strategy to solve a nonconvex optimization problem but utilize different methods for the inner block descent updates. We refer to the method in Liu, Chi, and Lange (2022+) as “MM” and the one in Chi and Chi (2022) as “PG” in our package. This package provides code to replicate some examples illustrating the usage of the frameworks in both manuscripts.

Installation

To install the latest stable version from CRAN:

install.packages('L2E')

To install the latest development version from GitHub:

# install.packages("devtools")
devtools::install_github('jocelynchi/L2E-package-demo')

Getting Started

We’ve included an introductory demo on how to use the L2E framework with examples from the accompanying journal manuscripts.

Citing the package

Please reference the following manuscripts when citing this package. Thank you!

@article{L2E-Chi,
  title={A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion},
  author={Chi, Jocelyn T. and Chi, Eric C.},
  journal={Journal of Computational and Graphical Statistics},
  pages={1--12},
  year={2022},
  publisher={Taylor \& Francis}
}

@article{L2E-Liu,
  title={A Sharper Computational Tool for L$_2$E  Regression},
  author={Liu, Xiaoqian and Chi, Eric C. and Lange, Kenneth},
  journal={arXiv preprint arXiv:2203.02993},
  year={2022}
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.