A statistical method for reducing the number of covariates in
    an analysis by evaluating Variable Importance Measures (VIMPs) derived
    from the Random Forest algorithm. It performs statistical tests on the
    VIMPs and outputs whether the covariate is significant along with the
    p-values.

The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. <doi:10.1093/imamci/4.2.143>.

The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values.

Estimating the Shapley values using the algorithm in 
    the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and 
    Wei Zheng (2024) <doi:10.1080/01621459.2023.2257364> "Fast Approximation 
    of the Shapley Values Based on Order-of-Addition Experimental Designs".
    You provide the data and define the value function, it retures the 
    estimated Shapley values based on sampling methods or experimental designs.

Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...

In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.

Functions to read and write ESRI shapefiles.