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GitHub commit activityGitHub contributorsGitHub last commitGitHub pull requestsGitHub repo sizeProject Status: Active – The project has reached a stable, usable state and is being actively developed. Current Version Open Issues

Overview

The junco R package contains analysis functions to create tables and listings used for clinical trial reporting. It complements the tern package by providing additional statistical analysis capabilities.

The package provides a range of functionality, such as:

  • Statistical analysis (ANCOVA, MMRM, Cox regression, Kaplan-Meier, CMH stratified proportion difference estimation)
  • Calculation of odds ratios, relative risks, and proportion differences
  • Event incidence rate analysis
  • Support optional SAS (to-nearest-value) and IEC (to-nearest-even) rounding
  • Frequency tabulations and summarizations
  • Reference-based multiple imputation (RBMI) for handling missing data
  • Production-ready RTF and DOCX exporter for listings and tables (see tt_to_tbldf and export_as_docx_j)
  • Creation of tables, listings, and graphs (TLGs)

Installation

Either you can install the stable CRAN version

install.packages("junco") #CRAN Release

Or you can install the development version from GitHub:

require('remotes')
remotes::install_github("johnsonandjohnson/junco", ref = "dev") #for the rolling dev release

Usage

To understand how to use this package, please refer to the vignettes (also available on the pkgdown site). You can list them locally with:

browseVignettes(package = "junco")

Key vignettes and what they cover:

TLG Catalog — browse examples and outputs

Explore a curated catalog of Tables, Listings, and Graphs (TLGs) generated with junco and companion tooling:

What you’ll find there:

  • Realistic TLG outputs to help you preview what specific functions produce.
  • Pointers to the underlying function calls and key arguments used to create each artifact.
  • Context on when to use each approach (e.g., by analysis type or endpoint).

Tip: Use the catalog alongside the vignettes above and the function reference to quickly identify the right helper for your analysis, then adapt the showcased code to your study’s data and conventions.