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 methods which accounts for any feature
  dependence, and thereby produces more accurate estimates of the true Shapley values.
  An accompanying 'Python' wrapper ('shaprpy') is available through PyPI.

Visualizations for SHAP (SHapley Additive exPlanations), such
    as waterfall plots, force plots, various types of importance plots,
    dependence plots, and interaction plots.  These plots act on a
    'shapviz' object created from a matrix of SHAP values and a
    corresponding feature dataset. Wrappers for the R packages 'xgboost',
    'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and
    'kernelshap' are added for convenience.  By separating visualization
    and computation, it is possible to display factor variables in graphs,
    even if the SHAP values are calculated by a model that requires
    numerical features. The plots are inspired by those provided by the
    'shap' package in Python, but there is no dependency on it.