Fast Iterative Filtering (FIF) with Portable FFT Backend.

RFIF — Fast Iterative Filtering for R

RFIF provides an R interface to a C implementation of Fast Iterative Filtering (FIF) for decomposing a univariate signal into intrinsic mode functions (IMFs) plus a residual.

Installation

Install from a source tarball:

install.packages("RFIF_1.0.tar.gz", repos = NULL, type = "source")

Fast FFT backend (FFTW3)

RFIF performs many FFTs during decomposition. To guarantee portability, the package ships with a self-contained fallback FFT that works everywhere, but it can be much slower.

For best performance, RFIF can use FFTW3 when available.

Backend selection

During installation:

• If FFTW3 is detected via pkg-config → RFIF enables the fast FFTW backend
• Otherwise → RFIF uses the portable fallback FFT (still fully functional)

When FFTW3 is found, installation prints:

Found fftw3 via pkg-config (enabling fast FFT).

Installing FFTW3 (recommended)

macOS (MacPorts)

sudo port install pkgconfig
sudo port install fftw-3

Verify:

pkg-config --modversion fftw3

macOS (Homebrew)

brew install pkg-config fftw

Linux (Ubuntu / Debian)

sudo apt-get install libfftw3-dev pkg-config

Windows (Rtools + MSYS2)

1. Install Rtools: https://cran.r-project.org/bin/windows/Rtools/

2. Open the Rtools MSYS2 shell, then install FFTW and pkg-config:

pacman -S mingw-w64-x86_64-fftw
pacman -S mingw-w64-x86_64-pkg-config

Verify:

pkg-config --modversion fftw3

Then install RFIF from source in R as usual.

Basic usage

library(RFIF)

t <- seq(0, 1, length.out = 1000)
x <- sin(2*pi*5*t) + 0.5*sin(2*pi*20*t)

res <- rfif(x)

str(res)

Returned object:

• imfs: numeric matrix (rows = IMFs, columns = time)
• residual: numeric vector (same length as input)
• nimf: integer number of IMFs

Reconstruction check

recon <- if (res$nimf > 0) colSums(res$imfs) + res$residual else res$residual
max(abs(x - recon))

Vignette

browseVignettes("RFIF")