Internal flare functions
sugm.likelihood(Sigma, Omega)
sugm.tracel2(Sigma, Omega)
sugm.cv(obj, loss=c("likelihood", "tracel2"), fold=5)
part.cv(n, fold)
sugm.clime.ladm.scr(Sigma, lambda, nlambda, n, d, maxdf, rho, shrink, prec,
max.ite, verbose)
sugm.tiger.ladm.scr(data, n, d, maxdf, rho, lambda, shrink, prec,
max.ite, verbose)
slim.lad.ladm.scr.btr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec,
intercept, verbose)
slim.sqrt.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec,
intercept, verbose)
slim.dantzig.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec,
intercept, verbose)
slim.lq.ladm.scr.btr(Y, X, q, lambda, nlambda, n, d, maxdf, rho, max.ite, prec,
intercept, verbose)
slim.lasso.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, max.ite, prec,
intercept, verbose)
Covariance matrix.
Inverse covariance matrix.
An object with S3 class returned from "sugm".
Type of loss function for cross-validation.
The number of folds for cross-validation.
The number of observations (sample size).
Dimension of data.
Maximal degree of freedom.
Grid of positive values for the regularization parameter lambda.
The number of the regularization parameter lambda.
Shrinkage of regularization parameter based on precision of estimation.
Value of augmented Lagrangian multiplier.
Stopping criterion.
Maximal value of iterations.
n by d data matrix.
Dependent variables in linear regression.
Design matrix in linear regression.
The vector norm used for the loss term.
Indicator of whether to include intercepts.
Tracing information printing is disabled if verbose = FALSE. The default value is TRUE.
Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Tuo Zhao <tourzhao@gatech.edu>
These are not intended for use by users.
sugm, slim and flare-package.