Package: ALDEx3
Title: Linear Models for Sequence Count Data
Version: 1.0.2
Authors@R: 
    c(person("Justin", "Silverman", email = "JustinSilverman@psu.edu", role = c("aut", "cre")),
      person("Greg", "Gloor", email = "ggloor@uwo.ca", role = c("aut")),
      person("Kyle", "McGovern", email = "kvm6065@psu.edu", role = c("aut", "ctb")))
Description: 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>. 
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: rBeta2009, testthat (>= 3.0.0), lmtest, sandwich, knitr,
        rmarkdown
Config/testthat/edition: 3
Imports: purrr, lme4, lmerTest, parallel, MASS, nlme, abind,
        matrixStats, methods, stats
Depends: R (>= 3.5)
LazyData: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-05 15:14:56 UTC; jds6696
Author: Justin Silverman [aut, cre],
  Greg Gloor [aut],
  Kyle McGovern [aut, ctb]
Maintainer: Justin Silverman <JustinSilverman@psu.edu>
Repository: CRAN
Date/Publication: 2026-02-05 16:00:06 UTC
Built: R 4.6.0; ; 2026-04-26 05:30:13 UTC; unix
