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.