Linear Models for Sequence Count Data R package ALDEx3 version 1.0.2 Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data. Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) <doi:10.1186/s13059-025-03609-3> and McGovern et al. (2025) <doi:10.1101/2025.08.05.668734>. Software Depends: R (>= 3.5) Imports: purrr, lme4, lmerTest, parallel, MASS, nlme, abind, matrixStats, methods, stats Suggests: rBeta2009, testthat (>= 3.0.0), lmtest, sandwich, knitr, rmarkdown Justin Silverman <JustinSilverman@psu.edu> Comprehensive R Archive Network (CRAN) Justin Silverman [aut, cre], Greg Gloor [aut], Kyle McGovern [aut, ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=ALDEx3/LICENSE) 2026-02-05 application/tgz https://CRAN.R-project.org/package=ALDEx3 doi:10.32614/CRAN.package.ALDEx3