Package: ALDEx3 1.3.0

ALDEx3: Linear Models for Sequence Count Data

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>.

Authors:Justin Silverman [aut, cre], Greg Gloor [aut], Kyle McGovern [aut, ctb]

ALDEx3_1.3.0.tar.gz
ALDEx3_1.3.0.zip(r-4.7-x86_64)ALDEx3_1.3.0.zip(r-4.6-x86_64)ALDEx3_1.3.0.zip(r-4.5-x86_64)
ALDEx3_1.3.0.tgz(r-4.6-x86_64)ALDEx3_1.3.0.tgz(r-4.6-arm64)ALDEx3_1.3.0.tgz(r-4.5-x86_64)ALDEx3_1.3.0.tgz(r-4.5-arm64)
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ALDEx3_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ALDEx3/json (API)

# Install 'ALDEx3' in R:
install.packages('ALDEx3', repos = c('https://jsilve24.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jsilve24/aldex3/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

7.14 score 10 stars 35 scripts 312 downloads 7 exports 37 dependencies

Last updated from:6ad6e25c8f. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK240
linux-devel-x86_64OK270
source / vignettesOK311
linux-release-arm64OK210
linux-release-x86_64OK281
macos-release-arm64OK193
macos-release-x86_64OK289
macos-oldrel-arm64OK214
macos-oldrel-x86_64OK542
windows-devel-x86_64OK268
windows-release-x86_64OK276
windows-oldrel-x86_64OK279
wasm-releaseOK136

Exports:aldexaldex.effectaldex.plotclr.smcoefficient.smsample.smtss.sm

Dependencies:abindbootclicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelme4lmerTestmagrittrMASSMatrixmatrixStatsminqanlmenloptrnumDerivpurrrR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7scalesTMBvctrsviridisLitewithr

ALDEx3-Quickstart
ALDEx3 | Overview | Installation | Quickstart (Inference with Relative Abundances) | Mixed Effects Models | Inference with Absolute Abundances: Defining Scale Models | Prior Knowledge About a Group Difference | External Measurements for Individual Samples | CLR with Scale Uncertainty | Model Outputs | Plotting pairwise analyses
Rendered fromALDEx3-Quickstart.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-01-13

ALDEx3 Mixed-Effects Engines
Motivation | What BLMM Approximates | Conceptual Formulation | Implemented Profiled Formulation | The response-dependent part of the draw-specific criterion is the profiledweighted residual sum of squares[\mathrm{PWRSS}_s(\theta_s) | More precisely, the anchor fit targets the average of the per-draw profiledREML criteria:[\bar{\ell}(\phi) | Conditional on (\phi_s), BLMM then solves the fixed effects exactly bygeneralized least squares:[\hat{\beta}_s | Assumptions and Scope | Validation Strategy | Worked Example | Example Comparison: lme4 Versus blmm | Runtime Scaling
Rendered fromALDEx3-mixed-effects.Rmdusingknitr::rmarkdown

Last update: 2026-07-15
Started: 2026-07-15