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>.

Justin Silverman

ALDEx3

Linear Models for Sequence Count Data

Greg Gloor

Kyle McGovern

req function

<dl><dt>obj</dt>
<dd>object (list type)</dd>
<dt>names</dt>
<dd>names of elements required to be in the list</dd></dl>

Arguments

Author

Test object contains elements — req

<dl>

<dt>obj</dt>
<dd>object (list type)</dd>


<dt>names</dt>
<dd>names of elements required to be in the list</dd>

</dl>

req: Test object contains elements

Description

Usage

Arguments

Author