Aid in visual data investigations
 using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. 
 It provides summary plot, dependence plot, interaction plot, and force plot and relies on
 the SHAP implementation provided by 'XGBoost' and 'LightGBM'.
 Please refer to 'slundberg/shap' for the original implementation of SHAP in 'Python'.

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers.
    Includes implementations of methods described in Mayrhofer and Filzmoser (2022) <doi:10.48550/ARXIV.2210.10063>.

Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.

Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.

Complex machine learning models are often hard to interpret. However, in 
  many situations it is crucial to understand and explain why a model made a specific 
  prediction. Shapley values is the only method for such prediction explanation framework 
  with a solid theoretical foundation. Previously known methods for estimating the Shapley 
  values do, however, assume feature independence. This package implements the method 
  described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature 
  dependence, and thereby produces more accurate estimates of the true Shapley values.