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CAFT  

Rank-Based Compositional Analysis using Log-Linear Models for Microbiome Data with Zero Cells
View on CRAN: Click here


Download and install CAFT package within the R console
Install from CRAN:
install.packages("CAFT")

Install from Github:
library("remotes")
install_github("cran/CAFT")

Install by package version:
library("remotes")
install_version("CAFT", "0.1.0")



Attach the package and use:
library("CAFT")
Maintained by
Mo Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-25
Latest Update: 2026-06-25
Description:
Provides rank-based compositional differential abundance analysis for microbiome count data with zero cells using the compositional accelerated failure time model of Satten, Li and Zhao (2025) "CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells" <doi:10.1101/2025.11.26.690468>. Zero counts are treated as censored observations below sample-specific detection limits, avoiding the use of pseudocounts. The package implements estimation and hypothesis testing procedures for assessing associations between microbial taxa and covariates while accounting for the compositional structure of sequencing count data. It supports taxon-level differential abundance analysis, estimation of regression effects under censoring induced by detection limits, and inference based on rank-based methods that remain applicable in the presence of excess zeros. Functions are provided for model fitting, significance testing, extraction of effect estimates, and summarization of results across taxa. The package also provides optional bootstrap calibration of taxon-level p-values for sensitivity analysis in small-taxon settings.
How to cite:
Mo Li (2026). CAFT: Rank-Based Compositional Analysis using Log-Linear Models for Microbiome Data with Zero Cells. R package version 0.1.0, https://cran.r-project.org/web/packages/CAFT. Accessed 24 Oct. 2026.
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