A collection of large language model (LLM) text analysis methods
  designed with psychological data in mind. Currently, LLMing (aka "lemming")
  includes a text anomaly detection method based on the angle-based subspace
  approach described by Zhang, Lin, and Karim (2015) and a text generation method.
  <doi:10.1016/j.ress.2015.05.025>.

Resolves ambiguous links between data.frames using large language models (LLMs). Supports matching across spelling variations, translations, and differing levels of precision.

Repairs malformed JSON strings, particularly those generated by
    Large Language Models. Handles missing quotes, trailing commas, unquoted
    keys, and other common JSON syntax errors.

One interface to many large language model providers: a single
  configuration object and a single calling function serve chat and embedding
  models alike, so research code does not change when the provider does. The
  same calls scale to multi-model and multi-condition studies.

Provides the shared shell that 'LLMR'-family graphical interfaces
    are built on. It includes provider and model selection,
    environment-variable-based API-key handling, demo and live runners, session
    cost accounting, error banners, comma-separated-value upload and column
    mapping, and display helpers for the shared diagnostics() and report()
    generics. Graphical interfaces for the method packages import these helpers
    rather than each reimplementing them, so a fix here is available to every
    interface that depends on it.

Large language model agents as governed research instruments,
  built on 'LLMR'. The package supports designed conversations and factorial
  experiments with declared tools and budgets. Each run produces an
  inspectable record that can be archived and checked.

A model-agnostic safety layer for developers building with
    large language model (LLM) applications. Maps starter controls to the Open
    Worldwide Application Security Project Top 10 for Large Language
    Model Applications 2025 risk categories
    <https://genai.owasp.org/llm-top-10/> via a modular rule engine.
    Supports regular-expression rules, lightweight natural language processing
    (NLP) intent checks, optional scanners, and semantic large language model
    reviewer checks on prompts, conversations, retrieved context, tool inputs
    and outputs, streaming chunks, and model outputs. Supports workflows with
    the 'Ollama' local web service <https://ollama.com/> via 'ellmer', remote
    reviewer endpoints, and other chat interfaces callable from 'R'.
    Intended as an experimental guardrail layer that teams should evaluate
    against their own workflows before relying on it in production.