vip - Variable Importance Plots

A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Last updated 1 years ago

interaction-effectmachine-learningpartial-dependence-plotsupervised-learning-algorithmsvariable-importancevariable-importance-plots

11.70 score 186 stars 7 packages 3.2k scripts 8.0k downloads

sure - Surrogate Residuals for Ordinal and General Regression Models

An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017, <doi:https://doi.org/10.1080/01621459.2017.1292915>) and Greenwell et al. (2017, <https://journal.r-project.org/archive/2018/RJ-2018-004/index.html>). These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available.

Last updated 5 years ago

categorical-datadiagnosticsordinal-regressionresiduals

5.06 score 9 stars 1 packages 43 scripts 583 downloads

KraljicMatrix - A Quantified Implementation of the Kraljic Matrix

Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>) for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and uses this information to place products and services within the Kraljic Matrix.

Last updated 7 years ago

decision-supportkraljic-matrixmanagerial-preferences

4.73 score 3 stars 18 scripts 216 downloads