spice/README.Rmd at master · Carol-seven/spice · GitHub

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---
output:
  github_document:
    pandoc_args: [ "--wrap=preserve", "--columns=80" ]
bibliography: inst/REFERENCES.bib
link-citations: yes
---

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

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```


# spice <img src="man/figures/logo.png" align="right" alt="" width="150"/>


## Sparse Precision (Inverse Covariance) Estimation


[![GitHub last commit](https://img.shields.io/github/last-commit/Carol-seven/spice)](https://github.com/Carol-seven/spice/commits/master)
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[![GitHub License](https://img.shields.io/github/license/Carol-seven/spice?color=blue)](https://github.com/Carol-seven/spice/blob/master/LICENSE.md)


The goal of **spice** is to provide classical statistical methods for estimating
sparse precision (inverse covariance) matrix for functional connectivity
analysis in brain networks, making these methods accessible and easy to use for
researchers and practitioners in neuroimaging.


## Methods


| Method | Reference |
|:--------|:----------|
| Graphical lasso (`method = "glasso"`) | @friedman2008sparse |
| Graphical ridge (`method = "ridge"`) | @vanwieringen2016ridge |
| Graphical elastic net (`method = "elnet"`) | @zou2005regularization |
| CLIME (`method = "clime"`) | @cai2011aconstrained |
| TIGER (`method = "tiger"`) | @liu2017tiger |
| Graphical adaptive lasso (`method = "adapt"`) | @zou2006adaptive; @fan2009network |
| Arctangent type penalty (`method = "atan"`) | @wang2016variable |
| Exponential type penalty (`method = "exp"`) | @wang2018variable |
| MCP (`method = "mcp"`) | @zhang2010nearly |
| SCAD (`method = "scad"`) | @fan2001variable; @fan2009network |


## Installation


You can install the development version of **spice** from
[GitHub](https://github.com/Carol-seven/spice) with:

``` r
# install.packages("pak")
pak::pkg_install("Carol-seven/spice")
```


## Example


```
library(spice)

set.seed(123)

X <- matrix(rnorm(200), 10, 20)

## Statistical methods for estimating the precision matrix,
## including the estimation and selection process
spice(X, method = "glasso", pkg = "glasso", crit = "CV", fold = 5)
```


## Reference {-}