Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.

Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.

Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event.  A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages.
    This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.

Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially 
    microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) 
    <doi:10.21037/jlpm.2019.04.05>).

bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
  are defined.
  For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  bioPN algorithms are developed in C to achieve adequate performance.