huge.tiger: Tuning-insensitive graph estimation

In huge: High-Dimensional Undirected Graph Estimation

Tuning-insensitive graph estimation

Description

See more details in huge

Usage

huge.tiger(
  x,
  lambda = NULL,
  nlambda = NULL,
  lambda.min.ratio = NULL,
  sym = "or",
  verbose = TRUE,
  input.type = "auto"
)

Arguments

x	There are 2 options: (1) x is an n by d data matrix (2) a d by d sample covariance matrix. The program automatically identifies the input matrix by checking the symmetry. (n is the sample size and d is the dimension).
lambda	A numeric scalar or non-empty one-dimensional numeric input defining a finite, strictly positive, non-increasing regularization path. Tied values are allowed. Leave lambda = NULL to generate the path from nlambda and lambda.min.ratio in C++.
nlambda	The number of regularization/thresholding parameters. The default value is 20 for method = "ct" and 10 for method = "mb", "glasso" or "tiger".
lambda.min.ratio	If method = "mb", "glasso" or "tiger", it is the smallest value for lambda, as a fraction of the upperbound (MAX) of the regularization/thresholding parameter which makes all estimates equal to 0. The program can automatically generate lambda as a sequence of length = nlambda starting from MAX to lambda.min.ratio*MAX in log scale. If method = "ct", it is the largest sparsity level for estimated graphs. The program can automatically generate lambda as a sequence of length = nlambda, which makes the sparsity level of the graph path increases from 0 to lambda.min.ratio evenly.The default value is 0.1 when method = "mb", "glasso" or "tiger", and 0.05 when method = "ct".
sym	Symmetrize the output graphs. If sym = "and", the edge between node i and node j is selected ONLY when both node i and node j are selected as neighbors for each other. If sym = "or", the edge is selected when either node i or node j is selected as the neighbor for each other. The default value is "or". ONLY applicable when method = "mb" or "tiger".
verbose	If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.
input.type	How to interpret x: "auto" preserves symmetry-based detection, "data" forces an observation matrix, and "covariance" requires a square covariance or correlation matrix. Correlation construction, covariance validation, and automatic lambda selection then occur together in C++.

Details

Raw observations are centered and normalized, while covariance input is converted to a correlation matrix, inside the shared C++ core. When lambda = NULL, the same native correlation matrix determines the returned default lambda path. A user-supplied path must be finite, strictly positive, and non-increasing; tied values are allowed and are used unchanged. If smaller generated values cannot be certified to the solver's KKT tolerance, the function warns and returns the longest certified path prefix. A user-supplied value that cannot be certified raises an error.

See Also

huge, and huge-package.

huge documentation built on Aug. 4, 2026, 5:09 p.m.