Gaussian Graphical Models with Nonconvex Regularization
Bootstrapped Edge Inclusion 'Probabilities'
Regression Coefficients from ggmncv Objects
Compare Edges Between Gaussian Graphical Models
Confirm Edges
Precision Matrix with Known Graph
De-Sparsified Graphical Lasso Estimator
Simulate a Partial Correlation Matrix
Extract Graph from ggmncv Objects
GGMncv: Gaussian Graphical Models with Nonconvex Regularization
GGMncv
Print the Head of eip Objects
Statistical Inference for Regularized Gaussian Graphical Models
Kullback-Leibler Divergence
Ledoit and Wolf Shrinkage Estimator
Network Comparison Test
Penalty Derivative
Penalty Function
Plot Edge Inclusion 'Probabilities'
Plot ggmncv Objects
Network Plot for select Objects
Plot penalty_derivative Objects
Plot penalty_function Objects
Predict method for ggmncv Objects
Print eip Objects
Print ggmncv Objects
Print nct Objects
Binary Classification
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>.