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.

M-estimators of location and shape following the power family 
    (Frahm, Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are 
    provided in the case of complete data and also when observations have 
    missing values together with functions aiding their visualization.

This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.  Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.