rLogDirichlet

Function for sampling Dirichlet random variables (base-2 normalized via log-sum-exp for stability)

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

rLogDirichlet function

<dl><dt>n</dt>
<dd>number of samples</dd>
<dt>alpha</dt>
<dd>D-vector of Dirichlet parameters</dd></dl>

Arguments

Author

Function for sampling Dirichlet random variables (base-2 normalized via log-sum-exp for stability) — rLogDirichlet

<dl>

<dt>n</dt>
<dd>number of samples</dd>


<dt>alpha</dt>
<dd>D-vector of Dirichlet parameters</dd>

</dl>

rLogDirichlet: Function for sampling Dirichlet random variables (base-2 normalized via log-sum-exp for stability)

Description

Usage

Value

Arguments

Author