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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-15Solvers: 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.
MIT. See LICENSE.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.