CVP_ADMMc: CV (no folds) ADMM penalized precision matrix estimation...
In SCPME: Shrinking Characteristics of Precision Matrix Estimators

Cross validation (no folds) function for shrink. This function is to be used with CVP_ADMM.

CVP_ADMMc(X_train, X_valid, Y_train, Y_valid, A, B, C, lam, alpha = 1,
  tau = 10, rho = 2, mu = 10, tau_rho = 2, iter_rho = 10L,
  crit = "ADMM", tol_abs = 1e-04, tol_rel = 1e-04, maxit = 10000L,
  adjmaxit = 10000L, crit_cv = "MSE", start = "warm",
  trace = "progress")

`X_train`	nxp training data matrix.
`X_valid`	(n - q)xp validation data matrix matrix.
`Y_train`	nxr training response matrix.
`Y_valid`	(n - q)xr validation response matrix.
`A`	option to provide user-specified matrix for penalty term. This matrix must have p columns. Defaults to identity matrix.
`B`	option to provide user-specified matrix for penalty term. This matrix must have p rows. Defaults to identity matrix.
`C`	option to provide user-specified matrix for penalty term. This matrix must have nrow(A) rows and ncol(B) columns. Defaults to identity matrix.
`lam`	positive tuning parameters for elastic net penalty. If a vector of parameters is provided, they should be in increasing order.
`alpha`	elastic net mixing parameter contained in [0, 1]. `0 = ridge, 1 = lasso`. Alpha must be a single value (cross validation across alpha not supported).
`tau`	optional constant used to ensure positive definiteness in Q matrix in algorithm
`rho`	initial step size for ADMM algorithm.
`mu`	factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size `rho` after each iteration.
`tau_rho`	factor in which to increase/decrease step size `rho`
`iter_rho`	step size `rho` will be updated every `iter.rho` steps
`crit`	criterion for convergence (`ADMM` or `loglik`). If `crit = loglik` then iterations will stop when the relative change in log-likelihood is less than `tol.abs`. Default is `ADMM` and follows the procedure outlined in Boyd, et al.
`tol_abs`	absolute convergence tolerance. Defaults to 1e-4.
`tol_rel`	relative convergence tolerance. Defaults to 1e-4.
`maxit`	maximum number of iterations. Defaults to 1e4.
`adjmaxit`	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. This option is intended to be paired with `warm` starts and allows for "one-step" estimators. Defaults to 1e4.
`crit_cv`	cross validation criterion (`MSE`, `loglik`, `penloglik` `AIC`, or `BIC`). Defaults to `MSE`.
`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`.