Methods and tools for implementing functional singular spectrum analysis and related techniques.

Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).

Provides and extends the 'Fuzzy Coco' algorithm by wrapping the
    'FuzzyCoCo' 'C++' Library, cf
    <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. 'Fuzzy Coco'
    constructs systems that predict the outcome of a human decision-making
    process while providing an understandable explanation of a possible
    reasoning leading to it.  The constructed fuzzy systems are composed
    of rules and linguistic variables.  This package provides a 'S3' classic
    interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip'
    interface, a custom engine with custom iteration stop criterion and
    progress bar support as well as a systematic implementation that do
    not rely on genetic programming but rather explore all possible
    combinations.

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>. 
    The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
    This gain is aggregated for each split variable in a tree and averaged across trees.

Sequential permutation testing for statistical
  significance of predictors in random forests and other prediction methods. 
  The main function of the package is rfvimptest(), which allows to test for 
  the   statistical significance of predictors in random forests using 
  different (sequential) permutation test strategies [1]. The advantage 
  of sequential over conventional permutation tests is that they
  are computationally considerably less intensive, as the sequential
  procedure is stopped as soon as there is sufficient evidence
  for either the null or the alternative hypothesis.
  Reference:
  [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation
	  testing of variable importance measures by the example of random forests.
	  Computational Statistics & Data Analysis 181:107689, <doi:10.1016/j.csda.2022.107689>.