Implements a generalized linear model approach for detecting
differentially expressed genes across treatment groups in count data. The
package supports both quasi-Poisson and negative binomial models to handle
over-dispersion, ensuring robust identification of differential expression.
It allows for the inclusion of treatment effects and gene-wise covariates,
as well as normalization factors for accurate scaling across samples.
Additionally, it incorporates statistical significance testing with
options for p-value adjustment and log2 fold range thresholds,
making it suitable for RNA-seq analysis as described in by
Xu et al., (2024)