Provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

Provides a unified interface for interacting with Large Language
    Models (LLMs) through various providers including OpenAI
    <https://platform.openai.com/docs/api-reference>, Ollama
    <https://ollama.com/>, and other OpenAI-compatible APIs. Features include
    automatic connection testing, max_tokens limit auto-adjustment, structured
    JSON responses with schema validation, interactive JSON schema generation,
    prompt templating, and comprehensive diagnostics.

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>.

Provides a unified interface to large language models across multiple
  providers. Supports text generation, structured output with optional JSON
  Schema validation, and embeddings. Includes tidyverse-friendly helpers,
  chat session, consistent error handling, and parallel batch tools.

A 'shiny' application to automate forward and back survey translation
    with optional reconciliation using large language models (LLMs). Supports
    both item-by-item and batch translation modes for optimal performance and
    context-aware translations. Handles multi-sheet Excel files and supports
    OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the
    TRAPD (Translation, Review, Adjudication, Pretesting, Documentation)
    framework and ISPOR (International Society for Pharmacoeconomics and
    Outcomes Research) recommendations. See Harkness et al. (2010)
    <doi:10.1002/9780470609927.ch7> and Wild et al. (2005)
    <doi:10.1111/j.1524-4733.2005.04054.x>.