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.

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>.

Identify the characteristics of patients in data mapped to the 
    Observational Medical Outcomes Partnership (OMOP) common data model.

Creation of patient profile visualizations for
  exploration, diagnostic or monitoring purposes during a clinical trial.
  These static visualizations display a patient-specific overview
  of the evolution during the trial time frame of 
  parameters of interest (as laboratory, ECG, vital signs),
  presence of adverse events, exposure to a treatment; 
  associated with metadata patient information, 
  as demography, concomitant medication.
  The visualizations can be tailored for specific domain(s) or endpoint(s) of interest.
  Visualizations are exported into patient profile report(s)
  or can be embedded in custom report(s).