A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including
    company and fund filings along with all associated metadata.

Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling <doi:10.1111/j.1467-8659.2009.01450.x>, a flow algorithm based on Steiner trees<doi:10.1080/15230406.2018.1437359> and a multicriteria optimization method for metro map layouts <doi:10.1109/TVCG.2010.24>.

Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.

Facilitates basic spatial edge correction to point pattern data.

Datasets from most recent CCIIO DIY entry
  in a tidy format. These support the Centers for Medicare and Medicaid
  Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows
  health insurance issuers to calculate member risk profiles under the Health
  and Human Services-Hierarchical Condition Categories (HHS-HCC) regression
  model. This regression model is used to calculate risk adjustment transfers.
  Risk adjustment is a selection mitigation program implemented under the
  Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA.
  Under the ACA, health insurance issuers submit claims data to CMS	in order
  for CMS to calculate a risk score under the HHS-HCC regression model.
  However, CMS does not inform issuers of their average risk score until after
  the data submission deadline. These data sets can be used by issuers to
  calculate their average risk score mid-year. More information about risk
  adjustment and the HHS-HCC model can be found here:
  <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.

Enables R users to run large language models locally using 'GGUF' model files
    and the 'llama.cpp' inference engine. Provides a complete R interface for loading models,
    generating text completions, extracting vector representations, and streaming responses in real-time.
    Includes grammar-constrained generation for structured output, text classification,
    and retrieval-augmented generation (RAG) pipelines. Supports local inference without
    requiring cloud APIs or internet connectivity, ensuring complete data privacy and
    control. Based on the 'llama.cpp' project by Georgi Gerganov (2023) <https://github.com/ggml-org/llama.cpp>.