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 fold for cross validatio.
The number of observations (sample size).
Dimension of data.
Maximal degree of freedom.
Grid of non-negative 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 multipiler.
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.
The indicator of whether including intercepts specifically.
Tracing information printing is disabled if verbose = FALSE. The default value is TRUE.
These are not intended for use by users.
sugm, slim and flare-package.