2  Packages

A short stack does most of the work in this book. ggplot2 (Wickham et al. 2026) is the foundation; everything else either builds figures on top of it or supplies the modeling output we plot.

library(ggplot2)
library(hvtiPlotR)        # house plots, themes, and PPT/PDF export
library(hvtiRutilities)   # data dictionaries, labels, manifests
library(gt)               # publication tables
library(patchwork)        # compose multi-panel figures

What each one is for:

• hvtiPlotR (Ehrlinger 2026b): the core of the book. It wraps the figures we publish into a two-step API: an hv_*() constructor validates the data and returns an object, and plot() turns that object into a bare ggplot. It also carries the house themes (theme_hv_manuscript(), theme_hv_poster(), the PowerPoint themes) and the save helpers.
• hvtiRutilities (Ehrlinger 2026c): the data-governance side: data dictionaries, variable labels, dataset manifests, and the synthetic-data generators we use so the examples run without touching real patient data.
• ggRandomForests (Ehrlinger 2026a), randomForestSRC (Ishwaran and Kogalur 2026; Ishwaran and Kogalur 2007; Ishwaran et al. 2008), and varPro (Lu et al. 2026): the random-forest stack. randomForestSRC fits the forest, varPro does variable selection, and ggRandomForests turns either one into a ggplot. These load in the random forest chapters where they are used.
• TemporalHazard (Ehrlinger 2026d): our R port of the SAS/C HAZARD module, for parametric and nonparametric hazard modeling. It loads in its own chapter.
• gt (Iannone et al. 2026): publication-quality tables. We use it directly for now; a companion hvtiRtables package is planned to give tables the same house treatment the plots get.
• patchwork (Pedersen 2025): composes several plots into one figure with shared labels and panel tags.
• survival (Therneau 2026; Terry M. Therneau and Patricia M. Grambsch 2000): the Surv() objects and Kaplan-Meier machinery underneath the survival figures.
• mlbench (Leisch and Dimitriadou 2026): example data only — the ROC chapter borrows its Pima Indians diabetes set for a classification forest that discriminates well enough to draw a ROC curve worth reading. Loaded just in that chapter.

You do not need all of these for any single figure. Each chapter loads what it uses in its first chunk.

Ehrlinger, John. 2026a. ggRandomForests: Visually Exploring Random Forests. https://github.com/ehrlinger/ggRandomForests.

Ehrlinger, John. 2026b. hvtiPlotR: HVTI Ggplot2 Themes and Clinical Plot Functions for the Cleveland Clinic Heart & Vascular Institute. https://github.com/ehrlinger/hvtiPlotR.

Ehrlinger, John. 2026c. hvtiRutilities: A Collection of Utilities for Use by HVTI CORR Group at the Cleveland Clinic. https://github.com/ehrlinger/hvtiRutilities.

Ehrlinger, John. 2026d. TemporalHazard: Temporal Parametric Hazard Modeling. https://ehrlinger.github.io/temporal_hazard/.

Iannone, Richard, Joe Cheng, Barret Schloerke, et al. 2026. Gt: Easily Create Presentation-Ready Display Tables. https://gt.rstudio.com.

Ishwaran, Hemant, and Udaya B. Kogalur. 2026. randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). https://www.randomforestsrc.org/.

Ishwaran, H., and U. B. Kogalur. 2007. “Random Survival Forests for r.” R News 7 (2): 25–31. https://CRAN.R-project.org/doc/Rnews/.

Ishwaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer. 2008. “Random Survival Forests.” Ann. Appl. Statist. 2 (3): 841–60. https://arXiv.org/abs/0811.1645v1.

Leisch, Friedrich, and Evgenia Dimitriadou. 2026. Mlbench: Machine Learning Benchmark Problems. https://doi.org/10.32614/CRAN.package.mlbench.

Lu, Min, Aster K. Shear, Udaya B. Kogalur, and Hemant Ishwaran. 2026. varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority. https://www.varprotools.org/.

Pedersen, Thomas Lin. 2025. Patchwork: The Composer of Plots. https://patchwork.data-imaginist.com.

Terry M. Therneau, and Patricia M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. Springer.

Therneau, Terry M. 2026. Survival: Survival Analysis. https://github.com/therneau/survival.

Wickham, Hadley, Winston Chang, Lionel Henry, et al. 2026. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://ggplot2.tidyverse.org.