Enables gene regulatory network (GRN) analysis on single cell clusters,
    using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>.
    Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' 
    implemented in 'snakemake'. Finally, incorporate results for visualization 
    and interpretation.

An interactive 'shiny' application for performing non-compartmental analysis (NCA) on
  pre-clinical and clinical pharmacokinetic data. The package builds on 'PKNCA' for core estimators and provides interactive visualizations, CDISC
  outputs ('ADNCA', 'PP', 'ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory
  analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core
  estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic
  Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.

Assigns genetic ancestry to an individual and
    studies relationships between local and global populations.

Performs AnchorRegression proposed by Rothenhäusler et al. 2020. 
    The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>)
    The code was developed independently from the authors of the paper.

Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) <doi:10.1093/biomet/asad008>) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) <doi:10.1515/jci-2024-0011>) with explicit error control for false discovery, at least asymptotically.

Construct language-aware lists. Make "and"-separated and
    "or"-separated lists that automatically conform to the user's language
    settings.

A decision support tool to strategically prioritise evidence gathering 
  in complex, hierarchical AND-OR decision trees. It is designed for situations 
  with incomplete or uncertain information where the goal is to reach a confident 
  conclusion as efficiently as possible (responding to the minimum number of 
  questions, and only spending resources on generating improved evidence when 
  it is of significant value to the final decision). The framework excels in 
  complex analyses with multiple potential successful pathways to a conclusion 
  ('OR' nodes). Key features include a dynamic influence index to guide users to 
  the most impactful question, a system for propagating answers and 
  semi-quantitative confidence scores (0-5) up the tree, and post-conclusion 
  guidance to identify the best actions to increase the final confidence. 
  These components are brought together in an interactive command-line workflow 
  that guides the analysis from start to finish.