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.

Visualisation of multidimensional data through different Andrews curves: 
   Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.

Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

Provides a comprehensive system for selecting
    variables and weighting data to match the specifications of the American
    National Election Studies. The package includes methods for identifying
    discrepant variables, raking data, and assessing the effects of the raking
    algorithm. It also allows automated re-raking if target variables fall
    outside identified bounds and allows greater user specification than other
    available raking algorithms. A variety of simple weighted statistics that
    were previously in this package (version .55 and earlier) have been moved to
    the package 'weights.'