Quantify Compositional Variability Across Relative Abundance Vectors

Implements the statistic FAVA, an Fst-based Assessment of Variability across vectors of relative Abundances, as well as a suite of helper functions which enable the visualization and statistical analysis of relative abundance data. The 'FAVA' R package accompanies the paper, “Quantifying compositional variability in microbial communities with FAVA” by Morrison, Xue, and Rosenberg (2025) .

FAVA

The FAVA R package implements the statistic FAVA, an FST-based Assessment of Variability across vectors of relative Abundances, as well as a suite of helper functions which enable the visualization and statistical analysis of relative abundance data. The FAVA R package accompanies the paper, “Quantifying compositional variability in microbial communities with FAVA” by Morrison et al. (2025).

The FAVA R package includes the following core functions:

• fava: Quantify variability across many compositional vectors in a single, normalized index, called FAVA

• bootstrap_fava: Compare values of FAVA between pairs of abundance matrices

• window_fava: Compute FAVA in sliding windows along the rows of a relative abundance matrix

• plot_relabund: Visualize a relative abundance matrix as a stacked bar plot

Installation

Install FAVA with:

install.packages("FAVA")

You can install the development version of FAVA from GitHub with:

# First, install devtools if you haven't already:
# install.packages("devtools")

devtools::install_github("MaikeMorrison/FAVA")

# If you wish to access the tutorial (also accessible below) from within
# the package:

devtools::install_github("MaikeMorrison/FAVA", build_vignettes = TRUE)

Tutorial

The package website, maikemorrison.github.io/FAVA/, contains documentation and examples for all package functions. It also contains a tutorial on the usage of FAVA for the analysis of microbiome data. The tutorial vignette is available at this link.