DEHOGT: Differentially Expressed Heterogeneous Overdispersion Gene Test
for Count Data
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) <doi:10.1371/journal.pone.0300565>.
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