Package: GGMncv
Type: Package
Title: Gaussian Graphical Models with Nonconvex Regularization
Version: 2.1.2
Date: 2026-4-28
Authors@R: c(person("Donald", "Williams", email = "drwwilliams@ucdavis.edu", role = "aut"),
	     person("Philippe", "Rast", email = "rast.ph@gmail.com", role = c("cre")))
Description: Estimate Gaussian graphical models with nonconvex penalties,
  including methods described by Williams (2020) <doi:10.31234/osf.io/ad57p>.
  Penalties include atan (Wang and Zhu, 2016) <doi:10.1155/2016/6495417>,
  seamless L0 (Dicker, Huang and Lin, 2013) <doi:10.5705/ss.2011.074>,
  exponential (Wang, Fan and Zhu, 2018) <doi:10.1007/s10463-016-0588-3>,
  smooth integration of counting and absolute deviation (Lv and Fan, 2009)
  <doi:10.1214/09-AOS683>, logarithm (Mazumder, Friedman and Hastie, 2011)
  <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation
  (Fan and Li, 2001) <doi:10.1198/016214501753382273>, and minimax concave
  penalty (Zhang, 2010) <doi:10.1214/09-AOS729>. The package also provides
  extensions for variable inclusion probabilities, multiple regression
  coefficients, and statistical inference (Janková and van de Geer, 2015)
  <doi:10.1214/15-EJS1031>.
License: GPL-2
Depends: R (>= 4.0.0)
Imports: Rcpp (>= 1.0.4.6), Rdpack (>= 0.11-1), reshape, GGally (>=
        1.4.0), ggplot2 (>= 3.3.0), glassoFast (>= 1.0), network (>=
        1.15), numDeriv (>= 2016.8-1.1), mathjaxr (>= 1.0-1), MASS (>=
        7.3-51.5), methods, parallel, pbapply, sna (>= 2.5), stats,
        utils
Suggests: car, corpcor, corrplot, dplyr, NetworkToolbox,
        NetworkComparisonTest, nlshrink, rmarkdown, knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
LinkingTo: Rcpp, RcppArmadillo
RdMacros: Rdpack, mathjaxr
BugReports: https://github.com/rast-lab/GGMncv/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-04-30 01:30:52 UTC; philippe
Author: Donald Williams [aut],
  Philippe Rast [cre]
Maintainer: Philippe Rast <rast.ph@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-04 19:00:24 UTC
