bregr : An R Package for Streamlined Batch Processing and Visualization of Biomedical Regression Models

作者：Shixiang Wang, Yun Peng, Chenyang Shu, Chunyang Wang, Yuxi Yang, Yankun Zhao, Yanru Cui, Dehua Hu, Jian‐Guo Zhou · 发表于：Med Research · 年份：2025 · DOI：10.1002/mdr2.70028 · 被引用次数：1 · 研究领域：Machine Learning in Healthcare、Radiomics and Machine Learning in Medical Imaging、Health, Environment, Cognitive Aging

ABSTRACT Regression analysis is essential in biomedical research for exploring relationships between phenotypic or clinical outcomes and diverse predictors. However, constructing multiple univariate and multivariate models is often hindered by the lack of robust tools for batch regression in R, forcing researchers to rely on custom scripts. To address this gap, we developed bregr , an open‐source R package built in the tidyverse style, leveraging the object‐oriented programming strategy for enhanced extensibility. bregr streamlines batch processing of diverse regression models, including generalized linear, Cox proportional hazards, and mixed‐effects, using native R pipes. It provides tidy outputs, integrated visualization, parallel computing capabilities, and a cohesive workflow, enabling efficient execution of hundreds of models with structured results for downstream analysis. Available on CRAN, bregr enhances efficiency, reproducibility, and scalability in biomedical research and beyond.