The jtools R package simplifies the presentation, automation, and interpretation of complex statistical models for data analysts and social scientists. If you are working with statistical data today, the tool streamlines tedious workflows by replacing dense, messy console output with publication-ready results.
Here are the 5 essential features of the jtools package available on the CRAN jtools page that you need to optimize your workflow: 1. Advanced Model Formatting (summ())
The summ() function replaces R’s basic summary() command with clean, highly customizable console outputs.
Clean layout: Drops unnecessary statistical jargon and defaults to readable tables. Flexible metrics: Lets you toggle R2cap R squared
, confidence intervals, and p-values on or off with simple arguments.
Multi-model support: Works seamlessly across standard linear models (lm), generalized linear models (glm), and mixed-effect models (lme4). 2. Built-In Robust Standard Errors
Calculating heteroskedasticity-robust standard errors typically requires chaining multiple external statistical packages together.
Instant calculation: Computes robust standard errors directly inside your summary functions using the sandwich package backend.
Stata-like ease: Mimics commercial software shortcuts by allowing you to specify standard error types (like HC3) as a quick argument. 3. Visualizing Predictions (effect_plot())
Instead of guessing the practical impact of your data based on standalone numeric coefficients, you can plot conditional effects instantly.
Data plotting: Visualizes model predictions against your raw observed data points.
Control variable accounting: Plots partial residuals to show a variable’s effect after controlling for other model factors.
Subgroup tracking: Handles continuous, polynomial, and complex categorical predictors automatically. 4. Seamless Table Exporting (export_summs())
Converting statistical summaries into presentation documents usually involves painful manual copying and pasting.
Side-by-side comparison: Combines multiple statistical models into a single comparative table.
Publication ready: Exports clean tables into Word, PDF/LaTeX, or RMarkdown formats via the huxtable backend.
Consistent formatting: Retains all data adjustments, including robust standard errors and standardized coefficients, during export. 5. Automated Data Standardization (gscale())
Interpreting models with different baseline units can distort your perception of which variable has the strongest impact.
Tools for summarizing and visualizing regression models – jtools
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