Efficient Serialization of R Objects

Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.

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.

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.

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) .

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.

Model Audit - Verification, Validation, and Error Analysis

Provides an easy to use unified interface for creating validation plots for any model. The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.

Kernel SHAP

Efficient implementation of Kernel SHAP, see Lundberg and Lee (2017), and Covert and Lee (2021) < http://proceedings.mlr.press/v130/covert21a>. Furthermore, for up to 14 features, exact permutation SHAP values can be calculated. The package plays well together with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'. Visualizations can be done using the R package 'shapviz'.