Helper functions designed to make
    dynamically generating R Markdown documents easier by providing a
    simple and tidy way to create report pieces, shape them to your data,
    and combine them for exporting into a single R Markdown document.

This algorithm is described in detail in the paper "Hedging Forecast Combinations With an Application to the Random Forest" by Beck et al. (2024) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5032102>. The package provides a function hedgedrf() that can be used to train a Hedged Random Forest model on a dataset, and a function predict.hedgedrf() that can be used to make predictions with the model.

Hedgehog will eat all your bugs.
  'Hedgehog' is a property-based testing package in the spirit
  of 'QuickCheck'. With 'Hedgehog', one can test properties
  of their programs against randomly generated input, providing
  far superior test coverage compared to unit testing. One of the
  key benefits of 'Hedgehog' is integrated shrinking of
  counterexamples, which allows one to quickly find the cause of
  bugs, given salient examples when incorrect behaviour occurs.

An implementation of the modelling and reporting features described 
    in reference textbook and guidelines (Briggs, Andrew, et al. Decision 
    Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011;
    Siebert, U. et al. State-Transition Modeling. Medical Decision Making 
    32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, 
    heterogeneity analysis, time dependency on state-time and model-time 
    (semi-Markov and non-homogeneous Markov models), etc.

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. 
  It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts.
  Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.