CVP_ADMM: Parallel CV (uses CV_ADMMc)
In ADMMsigma: Penalized Precision Matrix Estimation via ADMM

Parallel implementation of cross validation.

CVP_ADMM(X = NULL, lam = 10^seq(-2, 2, 0.2), alpha = seq(0, 1, 0.2),
  diagonal = FALSE, rho = 2, mu = 10, tau.inc = 2, tau.dec = 2,
  crit = c("ADMM", "loglik"), tol.abs = 1e-04, tol.rel = 1e-04,
  maxit = 1000, adjmaxit = NULL, K = 5, crit.cv = c("loglik",
  "penloglik", "AIC", "BIC"), start = c("warm", "cold"), cores = 1,
  trace = c("progress", "print", "none"))

`X`	nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
`lam`	positive tuning parameters for elastic net penalty. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values `10^seq(-2, 2, 0.2)`.
`alpha`	elastic net mixing parameter contained in [0, 1]. `0 = ridge, 1 = lasso`. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values `seq(-1, 1, 0.2)`.
`diagonal`	option to penalize the diagonal elements of the estimated precision matrix (Ω). Defaults to `FALSE`.
`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.inc`	factor in which to increase step size `rho`
`tau.dec`	factor in which to decrease step size `rho`
`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 1e3.
`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 (for each `alpha`). This option is intended to be paired with `warm` starts and allows for 'one-step' estimators. Defaults to NULL.
`K`	specify the number of folds for cross validation.
`crit.cv`	cross validation criterion (`loglik`, `penloglik`, `AIC`, or `BIC`). Defaults to `loglik`.
`start`	specify `warm` or `cold` start for cross validation. Default is `warm`.
`cores`	option to run CV in parallel. Defaults to `cores = 1`.
`trace`	option to display progress of CV. Choose one of `progress` to print a progress bar, `print` to print completed tuning parameters, or `none`.

returns list of returns which includes:

`lam`	optimal tuning parameter.
`alpha`	optimal tuning parameter.
`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).