use Rmd to create README files

Author: Carol-seven

.Rbuildignore

@@ -5,3 +5,4 @@
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
+^README\.Rmd$

README.Rmd

@@ -0,0 +1,82 @@
+---
+output:
+  github_document:
+    pandoc_args: [ "--wrap=preserve", "--columns=80" ]
+bibliography: inst/REFERENCES.bib
+---
+
+<!-- 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%"
+)
+```
+
+
+# grasps <img src="man/figures/logo.png" align="right" alt="" width="150">
+
+
+## Groupwise Regularized Adaptive Sparse Precision Solution
+
+
+<!-- badges: start -->
+[![GitHub R package version](https://img.shields.io/github/r-package/v/Carol-seven/grasps?label=R%20in%20dev&color=green)](https://github.com/Carol-seven/grasps/blob/main/DESCRIPTION)
+[![GitHub last commit](https://img.shields.io/github/last-commit/Carol-seven/grasps)](https://github.com/Carol-seven/grasps/commits/main)
+[![R-CMD-check](https://github.com/Carol-seven/grasps/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Carol-seven/grasps/actions/workflows/R-CMD-check.yaml)
+[![GitHub License](https://img.shields.io/github/license/Carol-seven/grasps?color=blue)](https://github.com/Carol-seven/grasps/blob/main/LICENSE.md)
+<!-- badges: end -->
+
+
+The goal of **grasps** is to provide a collection of statistical methods that
+incorporate both element-wise and group-wise penalties to estimate a precision
+matrix, making them user-friendly and useful for researchers and practitioners.
+
+
+## Penalties
+
+
+The package **grasps** provides functions to estimate precision matrices using
+the following penalties:
+
+
+1. Adaptive lasso [@zou2006adaptive;@fan2009network]
+
+2. Lasso [@tibshirani1996regression;@friedman2008sparse]
+
+3. Minimax concave penalty (MCP) [@zhang2010nearly]
+
+4. Smoothly clipped absolute deviation (SCAD) [@fan2001variable;@fan2009network]
+
+
+## Installation
+
+
+You can install the development version of **grasps** from
+[GitHub](https://github.com/) with:
+
+
+```
+# install.packages("devtools")
+devtools::install_github("Carol-seven/grasps")
+```
+
+
+## Example
+
+
+```
+library(grasps)
+
+X <- matrix(rnorm(200), 10, 20)
+membership <- c(rep(1,5), rep(2,5), rep(3,4), rep(4,6))
+
+grasps(X, membership = membership, penalty = "lasso", crit = "BIC")
+```
+
+
+## Reference {-}
+

README.md

@@ -1,38 +1,90 @@
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
 # grasps <img src="man/figures/logo.png" align="right" alt="" width="150">
 
+## Groupwise Regularized Adaptive Sparse Precision Solution
+
+<!-- badges: start -->
 
 [![GitHub R package version](https://img.shields.io/github/r-package/v/Carol-seven/grasps?label=R%20in%20dev&color=green)](https://github.com/Carol-seven/grasps/blob/main/DESCRIPTION)
 [![GitHub last commit](https://img.shields.io/github/last-commit/Carol-seven/grasps)](https://github.com/Carol-seven/grasps/commits/main)
 [![R-CMD-check](https://github.com/Carol-seven/grasps/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Carol-seven/grasps/actions/workflows/R-CMD-check.yaml)
 [![GitHub License](https://img.shields.io/github/license/Carol-seven/grasps?color=blue)](https://github.com/Carol-seven/grasps/blob/main/LICENSE.md)
-
+<!-- badges: end -->
 
 The goal of **grasps** is to provide a collection of statistical methods that
 incorporate both element-wise and group-wise penalties to estimate a precision
 matrix, making them user-friendly and useful for researchers and practitioners.
 
+## Penalties
 
-## Installation
+The package **grasps** provides functions to estimate precision matrices using
+the following penalties:
+
+1.  Adaptive lasso (Zou 2006; Fan et al. 2009)
+
+2.  Lasso (Tibshirani 1996; Friedman et al. 2008)
+
+3.  Minimax concave penalty (MCP) (Zhang 2010)
 
+4.  Smoothly clipped absolute deviation (SCAD) (Fan and Li 2001; Fan et al. 2009)
+
+## Installation
 
 You can install the development version of **grasps** from
 [GitHub](https://github.com/) with:
 
-``` r
-# install.packages("devtools")
-devtools::install_github("Carol-seven/grasps")
-```
-
+    # install.packages("devtools")
+    devtools::install_github("Carol-seven/grasps")
 
 ## Example
 
+    library(grasps)
+
+    X <- matrix(rnorm(200), 10, 20)
+    membership <- c(rep(1,5), rep(2,5), rep(3,4), rep(4,6))
+
+    grasps(X, membership = membership, penalty = "lasso", crit = "BIC")
+
+## Reference
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+
+<div id="ref-fan2009network" class="csl-entry">
+
+Fan, Jianqing, Yang Feng, and Yichao Wu. 2009. “Network Exploration via the Adaptive LASSO and SCAD Penalties.” *The Annals of Applied Statistics* 3 (2): 521–41. <https://doi.org/10.1214/08-aoas215>.
+
+</div>
+
+<div id="ref-fan2001variable" class="csl-entry">
+
+Fan, Jianqing, and Runze Li. 2001. “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties.” *Journal of the American Statistical Association* 96 (456): 1348–60. <https://doi.org/10.1198/016214501753382273>.
+
+</div>
+
+<div id="ref-friedman2008sparse" class="csl-entry">
+
+Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2008. “Sparse Inverse Covariance Estimation with the Graphical Lasso.” *Biostatistics* 9 (3): 432–41. <https://doi.org/10.1093/biostatistics/kxm045>.
+
+</div>
+
+<div id="ref-tibshirani1996regression" class="csl-entry">
+
+Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” *Journal of the Royal Statistical Society: Series B (Methodological)* 58 (1): 267–88. <https://doi.org/10.1111/j.2517-6161.1996.tb02080.x>.
+
+</div>
+
+<div id="ref-zhang2010nearly" class="csl-entry">
+
+Zhang, Cun-Hui. 2010. “Nearly Unbiased Variable Selection Under Minimax Concave Penalty.” *The Annals of Statistics* 38 (2): 894–942. <https://doi.org/10.1214/09-AOS729>.
+
+</div>
 
-``` r
-library(grasps)
+<div id="ref-zou2006adaptive" class="csl-entry">
 
-X <- matrix(rnorm(200), 10, 20)
-groups <- c(rep(1,5), rep(2,5), rep(3,4), rep(4,6))
+Zou, Hui. 2006. “The Adaptive Lasso and Its Oracle Properties.” *Journal of the American Statistical Association* 101 (476): 1418–29. <https://doi.org/10.1198/016214506000000735>.
 
-sggm(X, groups = groups, penalty = "lasso", crit = "BIC")
-```
+</div>
 
+</div>