Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.

'FRACTRAN' is an obscure yet tantalizing programming language invented by John Conway of 'Game of Life' fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .

Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.

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>).

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>.