ggm_inference: Gaussian graphical model: statistical inference
In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

Learn the conditional dependence structure with null hypothesis significance testing. This provides a valid measure of parameter uncertainty.

ggm_inference(
  Y,
  alpha = 0.05,
  control_precision = FALSE,
  boot = TRUE,
  B = 1000,
  cores = 1,
  method = "pearson",
  progress = TRUE
)

`Y`	The data matrix of dimensions n (observations) by p (nodes).
`alpha`	The desired significance level (defaults to `0.05`). Note that 1 - alpha corresponds to specificity.
`control_precision`	Logical. Should precision (i.e., 1 - false discovery rate) be controlled at the level alpha (defaults to `FALSE`) ?
`boot`	Logical. Should a non-parametric bootstrap be employed (defaults to `TRUE`)?
`B`	Integer. Number of bootstrap replicates (defaults to `1000`).
`cores`	Integer. Number of cores to be used when executing in parallel (defaults to 1).
`method`	Character string. Which type of correlation coefficients to be computed. Options include `"pearson"` (default), `"kendall"`, `"spearman"`, and `"polychoric"`.
`progress`	Logical. Should a progress bar be included (defaults to `TRUE`)?

An object of class ggm_inference including:

wadj: Weighted adjacency matrix, corresponding to the partial correlation network.
adj: Adjacency matrix (detected effects).
pcors: Partial correlations.
n: Sample size.
p: Number of nodes.
Y: Data.