CV_GLASSOc: CV penalized precision matrix estimation (c++)
In MGallow/GLASSOO: Lasso Penalized Precision Matrix Estimation

Description Usage Arguments Value

Cross validation function for GLASSO.

CV_GLASSOc(X, S, lam, diagonal = FALSE, path = FALSE, crit_out = "avg",
  crit_in = "loss", tol_out = 1e-04, tol_in = 1e-04, maxit_out = 10000L,
  maxit_in = 10000L, adjmaxit_out = 10000L, K = 5L, crit_cv = "loglik",
  start = "warm", trace = "progress")

`X`	option to provide a nxp matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
`S`	option to provide a pxp sample covariance matrix (denominator n). If argument is `NULL` and `X` is provided instead then `S` will be computed automatically.
`lam`	positive tuning parameters for elastic net penalty. If a vector of parameters is provided, they should be in increasing order.
`diagonal`	option to penalize the diagonal elements of the estimated precision matrix (Ω). Defaults to `FALSE`.
`path`	option to return the regularization path. This option should be used with extreme care if the dimension is large. If set to TRUE, cores will be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE.
`crit_out`	criterion for convergence in outer (blockwise) loop. Criterion `avg` will loop until the average absolute parameter change is less than `tol_out` times tolerance multiple. Criterion `max` will loop until the maximum change in the estimated Sigma after an iteration over the parameter set is less than `tol_out`. Defaults to `avg`.
`crit_in`	criterion for convergence in inner (lasso) loop. Criterion for convergence. Criterion `loss` will loop until the relative change in the objective for each response after an iteration is less than `tol_in`. Criterion `avg` will loop until the average absolute change for each response is less than `tol_in` times tolerance multiple. Similary, criterion `max` will loop until the maximum absolute change is less than `tol_in` times tolerance multiple. Defaults to `loss`.
`tol_out`	convergence tolerance for outer (blockwise) loop. Defaults to 1e-4.
`tol_in`	convergence tolerance for inner (lasso) loop. Defaults to 1e-4.
`maxit_out`	maximum number of iterations for outer (blockwise) loop. Defaults to 1e4.
`maxit_in`	maximum number of iterations for inner (lasso) loop. Defaults to 1e4.
`adjmaxit_out`	adjusted maximum number of iterations. During cross validation this option allows the user to adjust the maximum number of iterations after the first `lam` tuning parameter has converged (for each `alpha`). This option is intended to be paired with `warm` starts and allows for 'one-step' estimators. Defaults to 1e4.
`K`	specify the number of folds for cross validation.
`crit_cv`	cross validation criterion (`loglik`, `AIC`, or `BIC`). Defaults to `loglik`.
`start`	specify `warm` or `cold` start for cross validation. Default is `warm`.
`trace`	option to display progress of CV. Choose one of `progress` to print a progress bar, `print` to print completed tuning parameters, or `none`.

list of returns includes:

`lam`	optimal tuning parameter.
`path`	array containing the solution path. Solutions will be ordered by ascending lambda values.
`min.error`	minimum average cross validation error (cv.crit) for optimal parameters.
`avg.error`	average cross validation error (cv.crit) across all folds.
`cv.error`	cross validation errors (cv.crit).