Module causality

Structs§

cmat_conj_t 🔒: Conjugate transpose of d×d complex matrix.
cmat_det 🔒: Complex matrix determinant, d×d.
cmat_inv 🔒: Complex matrix inverse via Gauss-Jordan, d×d row-major.
cmat_mul 🔒: Complex matrix multiply: C = A * B, all d×d row-major.
conditional_granger_causality: Conditional Granger causality (Geweke 1984). Tests if source Granger-causes target controlling for condition.
directed_transfer_function: Directed transfer function (Kaminski & Blinowska 1991). Returns (d × d × n_freqs) flat Vec.
identity_flat 🔒
pairwise_granger_causality: Pairwise Granger causality (Granger 1969). Returns log-likelihood ratio. Positive = source Granger-causes target.
partial_directed_coherence: Partial directed coherence (Baccala & Sameshima 2001). Returns (d × d × n_freqs) flat Vec.
solve_linear 🔒: Solve A x = b via Gaussian elimination with partial pivoting. A is n×n (row-major flat), b is n-length. Returns x.
solve_matrix 🔒: Solve A X = B where A is n×n and B is n×m. Returns X (n×m, row-major).
spectral_granger_causality: Spectral Granger causality (Geweke 1982). Returns (d × d × n_freqs) as flat Vec, row-major in [i][j][f] order.
sse_ols 🔒: Sum of squared errors for OLS regression: SSE = ||y - X beta||^2.
var_coefficients 🔒: Fit VAR(order) model. Returns (beta [order*d × d, row-major], sigma [d×d, row-major]).