README.md
In GFT: Generalized Fisher Transformation of Correlation Matrices

GFT (R package)

Forward and inverse generalized Fisher transformation (GFT) of correlation matrices in base R.

The GFT maps a non-singular n x n correlation matrix C to the unrestricted vector gamma = vecl(log C) in R^d, d = n(n-1)/2, and is a bijection onto R^d (Archakov and Hansen, 2021, Econometrica). The inverse is computed by GFT-FP+N (Archakov and Hansen, 2026): a fixed-point phase in the log domain followed by a matrix-free inexact Newton phase with preconditioned conjugate gradients.

library(GFT)
C <- 0.9^abs(outer(1:5, 1:5, "-"))
z <- gft(C)          # forward: correlation matrix -> R^10
r <- inv_gft(z)      # inverse: R^10 -> correlation matrix
max(abs(r$C - C))    # ~1e-15

Solvers: inv_gft (GFT-FP+N, recommended), inv_gft_fp (fixed point), inv_gft_broyden (Chen, Fei and Yu, 2025), inv_gft_newton (full Newton). All report eigendecomposition counts and convergence diagnostics.

# from CRAN (once accepted)
install.packages("GFT")

# development version
remotes::install_github("reinhardhansen/GFT", subdir = "r")

R/GFT.R is a line-faithful port of the Julia reference implementation (julia/src/GFT.jl, same repository). The test suite includes golden values generated by an independent NumPy implementation and shared with the Julia tests, so a passing run is a cross-language verification. dev/ contains a development harness (webR) and a script comparing solver iteration counts against the serialized draws used in the paper's supplement; it is excluded from the built package.

Archakov, I. and Hansen, P. R. (2021). A new parametrization of correlation matrices. Econometrica, 89(4), 1699-1715. doi:10.3982/ECTA16910
Archakov, I. and Hansen, P. R. (2026). A variational approach to the generalized Fisher transformation of correlation matrices. Working paper.
Chen, H., Fei, Y. and Yu, J. (2025). Multivariate stochastic volatility models based on generalized Fisher transformation. Journal of Econometrics, 251, 106041.

MIT. See LICENSE.