cov_to_path

input matrix, with rows and columns in desired topological ordering
Must be an invertible square matrix

sigma

character vector containing the dimension names of the input matrix
in the desired ordering

ordering

the number of digits used to round the output

digits

Helper function. Takes a covariance matrix and ordering and generates a lower-triangular weights matrix.

Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix.
This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools.
The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone.
The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.

cov_to_path: Path model from covariance matrix with ordering

Description

Usage

Arguments

Value

See Also