Inferring Age-Dependent Disease Topic from Diagnosis Data R package AgeTopicModels version 0.3.0 We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individual’s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individual’s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>. Software Depends: R (>= 3.5) Imports: data.table, dplyr, ggplot2, ggrepel, grDevices, gtools, magrittr, pROC, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: testthat (>= 3.0.0) Xilin Jiang <jiangxilin1@gmail.com> Comprehensive R Archive Network (CRAN) Xilin Jiang [aut, cre] (ORCID: <https://orcid.org/0000-0001-6773-9182>) MIT + file LICENSE (https://CRAN.R-project.org/package=AgeTopicModels/LICENSE) 2026-02-28 application/tgz https://CRAN.R-project.org/package=AgeTopicModels doi:10.32614/CRAN.package.AgeTopicModels