A simple countdown timer for slides and HTML documents
    written in 'R Markdown' or 'Quarto'. Integrates fully into 'Shiny'
    apps. Countdown to something amazing.

Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

Modular and unified R6-based interface for counterfactual explanation methods.
    The following methods are currently implemented: Burghmans et al. (2022) <doi:10.48550/arXiv.2104.07411>, 
    Dandl et al. (2020) <doi:10.1007/978-3-030-58112-1_31> and Wexler et al. (2019) <doi:10.1109/TVCG.2019.2934619>.
    Optional extensions allow these methods to be applied to a variety of models and use cases.
    Once generated, the counterfactuals can be analyzed and visualized by provided functionalities.
    The package is described in detail in Dandl et al. (2025) <doi:10.18637/jss.v115.i09>.

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.