Randomization-Based Inference for customized experiments.
  Computes Fisher-Exact P-Values alongside null randomization 
  distributions. Retrieves counternull sets and generates counternull 
  distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. 
  Package includes visualization of randomization distributions and Fisher 
  Intervals. Users can input custom test statistics and their own methods for 
  randomization. 
  Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.

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>.