Deploying to gh-pages from @ 9b5709b 🚀

Author: Carol-seven

404.html

@@ -48,8 +48,8 @@ therefore given by \\\vert E(\Omega) \vert\\.
 where \\\xi \in \[0,1\]\\ is a tuning parameter. Setting \\\xi = 0\\
 reduces EBIC to the classic BIC.
 
-4.  HBIC: High dimensional Bayesian information criterion ([Wang, Kim,
-    and Li 2013](#ref-wang2013calibrating); [Fan et al.
+4.  HBIC: High dimensional Bayesian information criterion ([Wang et al.
+    2013](#ref-wang2013calibrating); [Fan et al.
     2017](#ref-fan2017high))
 
 \\ \hat{\Omega}\_{\mathrm{HBIC}} = {\arg\min}\_{\Omega} \left\\
@@ -79,7 +79,7 @@ Figure 1: \\K\\-fold cross-validation procedure for tuning (\\\lambda\\,
 Akaike, Hirotogu. 1973. “Information Theory and an Extension of the
 Maximum Likelihood Principle.” In *Second International Symposium on
 Information Theory*, edited by Boris Nikolaevich Petrov and Frigyes
-Csáki, 267–81. Budapest, Hungary: Akadémiai Kiadó.
+Csáki. Akadémiai Kiadó.
 
 Chen, Jiahua, and Zehua Chen. 2008. “Extended Bayesian Information
 Criteria for Model Selection with Large Model Spaces.” *Biometrika* 95
@@ -93,9 +93,8 @@ Royal Statistical Society Series B: Statistical Methodology* 79 (2):
 Foygel, Rina, and Mathias Drton. 2010. “Extended Bayesian Information
 Criteria for Gaussian Graphical Models.” In *Advances in Neural
 Information Processing Systems 23 (NIPS 2010)*, edited by J. Lafferty,
-C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta, 604–12. Red
-Hook, NY, USA: Curran Associates, Inc.
-<https://dl.acm.org/doi/10.5555/2997189.2997257>.
+C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta. Curran
+Associates, Inc. <https://dl.acm.org/doi/10.5555/2997189.2997257>.
 
 Schwarz, Gideon. 1978. “Estimating the Dimension of a Model.” *The
 Annals of Statistics* 6 (2): 461–64.

LICENSE.html

@@ -71,12 +71,12 @@ where:
 ## Penalties
 
 1.  Lasso: Least absolute shrinkage and selection operator ([Tibshirani
-    1996](#ref-tibshirani1996regression); [Friedman, Hastie, and
-    Tibshirani 2008](#ref-friedman2008sparse))
+    1996](#ref-tibshirani1996regression); [Friedman et al.
+    2008](#ref-friedman2008sparse))
 
 \\\lambda p(\omega\_{ij}) = \lambda\vert\omega\_{ij}\vert.\\
 
-2.  Adaptive lasso ([Zou 2006](#ref-zou2006adaptive); [Fan, Feng, and Wu
+2.  Adaptive lasso ([Zou 2006](#ref-zou2006adaptive); [Fan et al.
     2009](#ref-fan2009network))
 
 \\ \lambda p\_\gamma(\omega\_{ij}) =
@@ -93,7 +93,7 @@ where:
 \arctan\left(\frac{\vert\omega\_{ij}\vert}{\gamma}\right), \quad \gamma
 \> 0. \\
 
-4.  Exp: Exponential type penalty ([Wang, Fan, and Zhu
+4.  Exp: Exponential type penalty ([Wang et al.
     2018](#ref-wang2018variable))
 
 \\ \lambda p\_\gamma(\omega\_{ij}) =
@@ -107,7 +107,7 @@ where:
 \\ \lambda p\_\gamma(\omega\_{ij}) =
 \lambda\vert\omega\_{ij}\vert^\gamma, \quad 0 \< \gamma \< 1. \\
 
-6.  LSP: Log-sum penalty ([Candès, Wakin, and Boyd
+6.  LSP: Log-sum penalty ([Candès et al.
     2008](#ref-candes2008enhancing))
 
 \\ \lambda p\_\gamma(\omega\_{ij}) =
@@ -123,8 +123,7 @@ where:
 \gamma\lambda. \end{cases} \quad \gamma \> 1. \\
 
 8.  SCAD: Smoothly clipped absolute deviation ([Fan and Li
-    2001](#ref-fan2001variable); [Fan, Feng, and Wu
-    2009](#ref-fan2009network))
+    2001](#ref-fan2001variable); [Fan et al. 2009](#ref-fan2009network))
 
 \\ \lambda p\_\gamma(\omega\_{ij}) = \begin{cases}
 \lambda\vert\omega\_{ij}\vert & \text{if } \vert\omega\_{ij}\vert \leq

articles/crit.html

@@ -5,19 +5,25 @@
 - **[Shiying Xiao](http://shiying-xiao.com)**. Author, maintainer.
   [](https://orcid.org/0000-0002-8846-3258)
 
+- **[Jun Yan](https://statcomp.org)**. Author.
+  [](https://orcid.org/0000-0003-4401-7296)
+
+- **[Panpan Zhang](https://panpan-zhang.com)**. Author.
+  [](https://orcid.org/0000-0002-8211-5930)
+
 ## Citation
 
 Source:
 [`DESCRIPTION`](https://github.com/Carol-seven/grasps/blob/main/DESCRIPTION)
 
-Xiao S (2025). *grasps: Groupwise Regularized Adaptive Sparse Precision
-Solution*. R package version 0.1.0,
+Xiao S, Yan J, Zhang P (2026). *grasps: Groupwise Regularized Adaptive
+Sparse Precision Solution*. R package version 0.1.0,
 <https://github.com/Carol-seven/grasps>.
 
     @Manual{,
       title = {grasps: Groupwise Regularized Adaptive Sparse Precision Solution},
-      author = {Shiying Xiao},
-      year = {2025},
+      author = {Shiying Xiao and Jun Yan and Panpan Zhang},
+      year = {2026},
       note = {R package version 0.1.0},
       url = {https://github.com/Carol-seven/grasps},
     }