print.eip

<p>An object of class <code>eip</code></p>

Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>,
including the 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 <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>. There are also extensions
for computing variable inclusion probabilities, multiple regression coefficients, and
statistical inference <doi:10.1214/15-EJS1031>.

Donald Williams

print.eip: Print `eip` Objects

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