README.Rmd

---
title: "sparseGraph"
output:
  md_document:
    variant: markdown_github
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
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:
```{r, eval = FALSE}
> 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](https://mirca.github.io), [D. P. Palomar](https://www.danielppalomar.com) (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](https://arxiv.org/abs/2006.14925).

## Links
[NeurIPS'20 Promotional slides](https://palomar.home.ece.ust.hk/papers/2020/YingCardosoPalomar-NIPS2020-slides.pdf)
[NeurIPS'20 Promotional video](https://www.youtube.com/watch?v=48IZzsMNF74)