Package: depictr 0.3.0

depictr: A Unified Toolkit for Visualising Statistical Models and Data

A cohesive, publication-ready toolkit of plots that span the whole analysis workflow with one consistent look. It covers exploratory data analysis (distributions, categorical summaries, bivariate plots, scatter-plot matrices, correlation heatmaps, missing-data maps, outliers, estimation statistics and descriptive tables); multivariate analysis, clustering with diagnostics and Kaplan-Meier survival curves; time series (trends, autocorrelation, decomposition, seasonality and forecasting); model estimates and inference (forest plots, model comparison, frequentist and Bayesian estimates, predicted values, interactions, random effects and optimiser checks); diagnostics and classification (residual panels, binned residuals, influence, quantile-quantile, receiver operating characteristic (ROC) curves, calibration, threshold tuning and confusion matrices); uncertainty and power; and reporting helpers (a shared theme, colourblind-aware palettes, plot composition and saving). Every plotting function returns a 'ggplot2' object (or a 'patchwork' object for composite panels), heavier modelling back-ends are optional, and the package ships with reproducibly simulated datasets so that every example and vignette runs without further setup.

Authors:Pablo Bernabeu [aut, cre]