Description Usage Arguments Value See Also Examples

Compute the varband estimates along a path of tuning parameter values.

1 2 | ```
varband_path(S, w = FALSE, lasso = FALSE, K = -1, lamlist = NULL,
nlam = 60, flmin = 0.01)
``` |

`S` |
The sample covariance matrix |

`w` |
Logical. Should we use weighted version of the penalty or not? If |

`lasso` |
Logical. Should we use l1 penalty instead of hierarchical group lasso penalty? Note that by using l1 penalty, we lose the banded structure in the resulting estimate. And when using l1 penalty, the becomes CSCS (Convex Sparse Cholesky Selection) introduced in Khare et al. (2016). Default value for |

`K` |
Integer between 0 and p - 1 (default), indicating the maximum bandwidth in the resulting estimate. A small value of K will result in a sparse estimate and small computing time. |

`lamlist` |
A list of non-negative tuning parameters |

`nlam` |
If lamlist is not provided, create a lamlist with length |

`flmin` |
if lamlist is not provided, create a lamlist with ratio of the smallest and largest lambda in the list. Default is 0.01. |

A list object containing

- path:
A array of dim (

`p`

,`p`

,`nlam`

) of estimates of L- lamlist:
a grid values of tuning parameters

1 2 3 4 5 6 | ```
set.seed(123)
n <- 50
true <- varband_gen(p = 50, block = 5)
x <- sample_gen(L = true, n = n)
S <- crossprod(scale(x, center = TRUE, scale = FALSE))/n
path_res <- varband_path(S = S, w = FALSE, nlam = 40, flmin = 0.03)
``` |

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