DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow

powerlmm R package for power calculations for two- and three-level longitudinal multilevel/linear mixed models.

Shiny App for Repeated Measurements Course

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

☂️ Scikit-longitudinal (Sklong) is an open-source Python library & Scikit-Learn API compliant, tailored to longitudinal machine learning classification tasks. It is ideal for researchers, data scientists, and analysts, as it provides specialist tools for dealing with repeated-measures data challenges

[CVPR'25] Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat

Latent Class Trajectory Models: An R Package

Online supplementary materials of “Three extensions of the random intercept cross-lagged panel model” by Mulder and Hamaker (2021).

An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods

R package for fitting joint models to time-to-event data and multivariate longitudinal data

R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.

The University of Pittsburgh English Language Institute Corpus (PELIC) dataset

Gaussian process regression + automatical model selection for logitudinal -omics data

R package for fitting joint models to time-to-event and longitudinal data

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

R-package for relational event models (one- and two-mode networks)

[npj Digital Medicine] "Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling" by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H Van Tassel, Kyle Kovacs, Emily Y Chew, Zhiyong Lu, Zhangyang Wang, & Yifan Peng

PyIOmica (pyiomica) is a Python package for omics analyses.

This repository has been transferred to jeroendmulder.github.io/RI-CLPM for easier maintenance. The Github Pages automatically redirects to the new Github Page.