Likelihood-based inference for skewed count distributions, typically of degrees used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis. See Jones and Handcock <doi:10.1098/rspb.2003.2369>.

Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.

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>.

A framework for the replicable removal of personally identifiable data 
    (PID) in data sets.  The package implements a suite of methods to suit different 
    data types based on the suggestions of  Garfinkel (2015) <doi:10.6028/NIST.IR.8053> 
    and the ICO "Guidelines on Anonymization" (2012) <https://ico.org.uk/media/1061/anonymisation-code.pdf>.