LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles

Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) ) in which interpretable inputs are created based on local rather than global behaviour of each original feature.

A Set of Datasets Used in My Classes or in the Book 'Modele Liniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych'

A set of datasets and functions used in the book 'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'. Datasets either come from real studies or are created to be as similar as possible to real studies.

Data Accompanying the Book "The Rogue's Guide to Linear Models"

Datasets and utilities for teaching linear models in the context of the BetaBit universe (StatPunk). The package provides simulated datasets based on the fictional LifeCalc algorithmic scoring system, illustrating concepts such as simple regression, ANOVA, ANCOVA, hierarchical models, multicollinearity, model selection (AIC, BIC), and regularisation (LASSO, Ridge). Each dataset is accompanied by a narrative context connecting statistical methodology to questions of algorithmic fairness and social consequence.

Local Interpretable (Model-Agnostic) Visual Explanations

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) . More details can be found in Staniak, Biecek (2018) .

Explore Correlations Between Variables in a Machine Learning Model

When exploring data or models we often examine variables one by one. This analysis is incomplete if the relationship between these variables is not taken into account. The 'corrgrapher' package facilitates simultaneous exploration of the Partial Dependence Profiles and the correlation between variables in the model. The package 'corrgrapher' is a part of the 'DrWhy.AI' universe.

Stochastic Gradient Descent log-Likelihood Estimation in Cox Proportional Hazards Model

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

Tools for Archiving, Managing and Sharing R Objects via GitHub

The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.

Variable Importance via Oscillations

Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile and global variable importance measure based on Partial Dependence Profiles.

Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

Builds generalized linear model with automatic data transformation. The 'xspliner' helps to build simple, interpretable models that inherits informations provided by more complicated ones. The resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm.

International Assessment Data Manager

Provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).