An 'RStudio' addin that integrates large language model (LLM)
    assistance directly into the code-editing workflow. Features include:
    (1) generate R code from inline comments; (2) obtain LLM-assisted fixes for
    console errors; (3) insert plain-English explanations of selected code
    blocks; (4) a multi-turn Chat Panel with session-context awareness
    (loaded packages, global objects, source editor contents, console history).
    Supports 'OpenAI', 'Anthropic' (Claude), 'DeepSeek', 'Groq',
    'Together AI', 'OpenRouter', 'Ollama' (fully local, no API key required),
    and any 'OpenAI'-compatible custom endpoint (e.g. 'LM Studio', 'vLLM',
    'llama.cpp').

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.

Provides missing data imputation through two complementary
    engines: a large language model engine that communicates with the
    'Anthropic' 'Claude' application programming interface for
    context-aware semantic imputation, and a fully self-contained offline
    engine implementing nineteen statistical and machine learning algorithms
    entirely in base R with no additional package dependencies. Offline
    methods include mean, median, mode, last observation carried forward,
    next observation carried backward, hot-deck, predictive mean matching,
    k-nearest neighbours, ordinary least-squares regression, Lasso with
    coordinate descent, Ridge with closed-form solution, Bayesian Ridge
    regression with evidence approximation following MacKay (1992), support
    vector regression with a radial basis function kernel, classification
    and regression trees, random forests, gradient boosting, iterative
    random forest imputation, principal component analysis imputation via
    iterative singular value decomposition, and nuclear-norm minimisation
    via singular value thresholding. When no API key is available the
    package automatically falls back to the offline engine, ensuring full
    operation in environments without internet access. Every imputed value
    is accompanied by a confidence score and a plain-language reasoning
    string, producing reproducible audit trails. The automatic method
    selector chooses the best algorithm per column based on data type,
    skewness, missingness rate, and inter-column correlations.