Linear Models for Sequence Count Data
Overview
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.
Maintenance
- Made BLMM reject invalid or ill-conditioned raw anchor Hessians instead of ridge-regularising and inverting them. Draw-specific Newton updates are now checked against their profiled REML objective, and rejected draws fall back selectively to exact
lme4rather than returning pathological uncertainty.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-30
- ERROR10 OK, 3 ERROR2026-07-27
- NOTE12 OK, 1 NOTE2026-07-16
- OK14 OK2026-03-13
- ERROR13 OK, 1 ERROR2026-03-12
Show 1 earlier changeHide 1 earlier change
- OK14 OK2026-03-10
Documentation
- Help pages
- 21
- Examples that run
- 80%4 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 71%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 14%
Downloads
Dependencies
Installing ALDEx3 adds 32 packages (17 with compiled code, 27 MB of source tarballs). Heaviest: ggplot2 6.0 MB, lme4 4.3 MB, nloptr 2.1 MB.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2026-01-31
- Total releases
- 6 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 25 KB / 2 files
- Download size
- 215 KB
Cite
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