covariance_matrix: Construct covariance and precision matrices
In spcorum/tsmvrdata: Datasets for tsmvr

Description Usage Arguments Value References

Constructs a covariance matrix and its associated precision matrix of the following types: first-order autoregressive [AR(1)], fractional gaussian noise [FGN], or scale free network [SFN].

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covariance_matrix(q, type = "AR1", rho = 0.7, h = 0.9, n_edge = 1,
  shift = 1, power = 1, zero_appeal = 1, g = 1, diag_val = 1,
  edge_val = 0.3)

`q`	dimension of covariance matrix (positive integer)
`type`	type of covariance matrix (string: 'AR1', 'FGN', or 'SFN')
`rho`	autoregression parameter for AR(1) covariance matrix (0 < `rho_err` < 1)
`h`	Hurst parameter for FGN covariance matrix (0 < `H` < 1)
`n_edge`	Barabasi algorithm number of edges per step for SFN covariance matrix (positive integer)
`shift`	eigenvalue shift parameter for SFN covariance matrix (`shift` > 0) (ensures matrix is PSD) )
`power`	scaling power for SFN covariance matrix (positive numeric)
`zero_appeal`	Barabasi algorithm baseline attractiveness for SFN covariance matrix (positive numeric)
`g`	number of hub nodes for HUB graph precision matrix (positive integer-valued numeric less than q)
`diag_val`	values of diagonal entries HUB graph precision matrix (non-negative numeric)
`edge_val`	values of HUB graph network edges