In mirca/sparseGraph: Estimating Graphs with Nonconvex, Sparse Promoting Regularizations

library(knitr)
opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.align = "center",
  fig.retina = 2,
  out.width = "75%",
  dpi = 96
)
knit_hooks$set(pngquant = hook_pngquant)

sparseGraph

It has been shown that L1-norm regularization does not recover sparse solutions in a Laplacian-constrained Gaussian Markov Random Field setting. sparseGraph provides a method to estimate sparse graphs via nonconvex regularization functions.

Installation

You can install the development version from GitHub:

> devtools::install_github("mirca/sparseGraph")

Microsoft Windows

On MS Windows environments, make sure to install the most recent version of Rtools.

Usage

Contributing

We welcome all sorts of contributions. Please feel free to open an issue to report a bug or discuss a feature request.

Citation

If you made use of this software please consider citing:

J. Ying, J. V. de M. Cardoso, D. P. Palomar (2020). Nonconvex Sparse Graph Learning under Laplacian-structured Graphical Model. Advances in Neural Information Processing Systems (NeurIPS'20).
J. Ying, J. V. de M. Cardoso, D. P. Palomar (2020). Does the l1-norm learn a sparse graphical model under Laplacian constraints? https://arxiv.org/abs/2006.14925.