print.eip

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>.

Philippe Rast

GGMncv

Gaussian Graphical Models with Nonconvex Regularization

Donald Williams

print.eip function

<dl><dt>x</dt>
<dd>An object of class <code>eip</code></dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Print eip Objects — print.eip

<dl>

<dt>x</dt>
<dd>An object of class <code>eip</code></dd>


<dt>...</dt>
<dd>Currently ignored.</dd>

</dl>

print.eip: Print `eip` Objects

Description

Usage

Value

Arguments