Lots of Superior Depictions

Create lots of colorful plots in a plethora of variations. Try the LSD demotour().

Speak for the Trees

Extracts decision rules from tree- and rule-based models fitted in 'R'. Rules are expressed as logical predicates that identify paths to terminal nodes, making model behavior more transparent and interpretable. Provides conversion methods to 'partykit' party objects for a wide range of model types. The 'partykit' infrastructure is described in Hothorn and Zeileis (2015) < https://jmlr.org/papers/v16/hothorn15a.html>.

Create Multi-Format Regression Tables

Create publication-ready regression tables in multiple formats, including 'Word', 'HTML', 'LaTeX', and 'PDF', from statistical models. Supports lm() and glm() models. Includes options for marginal effects, control variable grouping, and robust standard errors using methods described in Zeileis (2004) . Tables can be exported to 'Word' via 'flextable' or to 'LaTeX' for 'PDF' output.

Binning Variables to Use in Logistic Regression

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) .

Conditional Inference Trees with Stacked Multiple Imputation

Implements the stacked-imputation workflow for conditional inference trees ('ctree') described in Sherlock et al. (2026) . When data contain missing values, multiply imputed datasets (e.g., from 'mice') are stacked vertically and a single 'ctree' is fit on the combined data. To correct for the artificially inflated sample size introduced by stacking, every node-level test statistic is divided by the number of imputations M, the node-level p-values are recomputed from the chi-squared reference distribution 'ctree' uses (including its multiplicity adjustment across candidate splitting variables), and the tree is compressed bottom-up (the Stack/M correction). Degrees of freedom are derived for each node and each candidate variable, so univariate, bivariate and higher-dimensional outcomes are all handled, as are unordered factor predictors, whose degrees of freedom depend on how many levels remain in a node. The result is a conservative but interpretable single tree that incorporates imputation uncertainty without requiring pooling of structurally different trees. Also exports stack_imputations(), rescale_statistic(), prune_stackM(), node_table() and report_ctreeMI() as standalone utilities. The underlying 'ctree' algorithm is provided by 'partykit' (Hothorn & Zeileis, 2015; Hothorn, Hornik & Zeileis, 2006 ).

Easy Visualization of Conditional Effects from Regression Models

Offers a flexible and user-friendly interface for visualizing conditional effects from a broad range of regression models, including mixed-effects and generalized additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(), betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from 'survival'). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . For beta regression using 'betareg', see Cribari-Neto and Zeileis (2010) . For mixed-effects models with 'lme4', see Bates et al. (2015) . For models using 'glmmTMB', see Brooks et al. (2017) . Methods for generalized additive models using 'mgcv' follow Wood (2017) .

Support for Compiling Examination Tasks using the 'exams' Package

The main aim is to further facilitate the creation of exercises based on the package 'exams' by Grün, B., and Zeileis, A. (2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.

Adversarial Robustness Attacks for Statistical Claims

Attacks fitted R model claims by searching for small plausible perturbations that make a target result disappear. The package focuses on claim-level fragility, smallest-kill reporting, and reproducible caveated robustness checks for ordinary fitted model objects. The methods draw on the fragility-index concept of Walsh et al. (2014) , multiverse analysis of Steegen et al. (2016) , specification-curve analysis of Simonsohn et al. (2020) , and robust covariance estimation of Zeileis (2004) .

Audit 'ggplot2' Visualizations for Accessibility and Best Practices

Audits 'ggplot2' visualizations for accessibility issues, misleading practices, and readability problems. Checks for color accessibility concerns including colorblind-unfriendly palettes, misleading scale manipulations such as truncated axes and dual y-axes, text readability issues like small fonts and overlapping labels, and general accessibility barriers. Provides comprehensive audit reports with actionable suggestions for improvement. Color vision deficiency simulation uses methods from the 'colorspace' package Zeileis et al. (2020) . Contrast calculations follow WCAG 2.1 guidelines (W3C 2018 < https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum>).

Intersectional Differential Item Functioning Analysis

A toolkit for detecting Differential Item Functioning (DIF) using Logistic Regression (LR) as described in Swaminathan and Rogers (1990) , the IRT Likelihood Ratio Test (LRT) following Thissen, Steinberg & Wainer (1993, ISBN:0-8058-0972-4), and model-based recursive partitioning (MOB) as implemented in 'strucchange' following Strobl, Kopf and Zeileis (2015) . Designed for both standard two-group and intersectional multi-group designs, 'iDIFr' prioritises effect size reporting alongside statistical significance, clear guidance on group construction, and interpretable output suitable for applied testing contexts. Built-in Intersectional Contrast Analysis (ICA) classifies items as amplified, pure-intersection, obscured, or none by comparing single-variable and intersectional analyses.