CV: Parallel Cross Validation
In MGallow/CVglasso: Lasso Penalized Precision Matrix Estimation

Description Usage Arguments Value

Parallel implementation of cross validation.

CV(X = NULL, S = NULL, lam = 10^seq(-2, 2, 0.2), diagonal = FALSE,
  path = FALSE, tol = 1e-04, maxit = 10000, adjmaxit = NULL, K = 5,
  crit.cv = c("loglik", "AIC", "BIC"), start = c("warm", "cold"),
  cores = 1, trace = c("progress", "print", "none"), ...)

`X`	option to provide a nxp data 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. Defaults to grid of values `10^seq(-2, 2, 0.2)`.
`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 must be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE.
`tol`	convergence tolerance. Iterations will stop when the average absolute difference in parameter estimates in less than `tol` times multiple. 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 NULL.
`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`.
`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`.
`...`	additional arguments to pass to `glasso`.

returns list of returns which includes:

`lam`	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).