For a given graph containing vertices, edges, and a signal 
    associated with the vertices, the 'PathwaySpace' package performs a convolution 
    operation, which involves a weighted combination of neighboring vertices and 
    their associated signals. The package uses a decay function to project these 
    signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' has various 
    applications, such as visualizing network data in a graphical format that 
    highlights the relationships and signal strengths between vertices. By 
    combining graph theory, signal processing, and visualization, 'PathwaySpace' 
    provides a way of representing graph data on a continuous projection space.
    Based on methods introduced in Tercan et al. (2025) 
    <doi:10.1016/j.xpro.2025.103681> and Ellrott et al. (2025) 
    <doi:10.1016/j.ccell.2024.12.002>.

Performs pathway enrichment analysis using a voting-based framework that integrates CpG–gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe bias—a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations.

Tools to generate synthetic patient-level test datasets in the
    Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
    Includes a chat-driven generator backed by large language models and an
    interactive 'shiny' designer for editing CDM test sets.

A user friendly way to create patient level prediction models using
  the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
  of interest and an outcome of interest, the package can use data in the Common
  Data Model to build a large set of features. These features can then be used to
  fit a predictive model with a number of machine learning algorithms. This is
  further described in Reps (2017) <doi:10.1093/jamia/ocy032>.