A large number of measurements generate count data. This is a statistical data type 
    that only assumes non-negative integer values and is generated by counting. Typically, counting 
    data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. 
    The number of DNA double-strand breaks can be counted in individual cells using various 
    bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of 
    the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. 
    Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software 
    offers functions for a comprehensive automated evaluation of distribution models of count 
    data. In addition to programmatic interaction, a graphical user interface (web server) 
    is included, which enables fast and interactive data-scientific analyses. The user is supported 
    in selecting the most suitable counting distribution for his own data set.

Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.

A set of functions for applying a 
    restricted linear algebra to the analysis of 
    count-based data. See the accompanying preprint
    manuscript: "Normalizing need not be the norm:
    count-based math for analyzing single-cell data"
    Church et al (2022) <doi:10.1101/2022.06.01.494334>
    This tool is specifically designed to analyze 
    count matrices from single cell RNA sequencing 
    assays. The tools implement several count-based
    approaches for standard steps in single-cell 
    RNA-seq analysis, including scoring genes and cells, 
    comparing cells and clustering, calculating differential 
    gene expression, and several methods for rank 
    reduction. There are many opportunities for further
    optimization that may prove useful in the analysis of
    other data. We provide the source code freely
    available at <https://github.com/shchurch/countland>
    and encourage users and developers to fork the code for 
    their own purposes.