Changelog • AIGENIE

AIGENIE 2.1.2

Exposed boot.iter and ncores in AIGENIE(), GENIE(), local_AIGENIE(), and local_GENIE(). The boot.iter default remains 500, matching the current reduction pipeline; ncores = NULL preserves EGAnet’s existing default core behavior.
Added a publication-ready filtering_audit with item-level filtering provenance for UVA and bootEGA decisions.
Added per-type reduction_summary outputs.
Added pre-reduction network-loading diagnostics for filtered items.
Added bundled GPT-5.4 items and frozen embeddings for reproducible GENIE examples.
Added an official filtering-audit vignette and pkgdown documentation site.

Restored correct UVA redundancy detection with current EGAnet versions.
Set bootEGA reduction to 500 bootstrap iterations.
Full embeddings now win exact full/sparse NMI ties within an EGA model.
run.overall = TRUE now performs a pooled post-reduction fit without applying a second item-reduction pass.

Updated Authors@R so Lara Russell-Lasalandra and Hudson Golino are explicitly represented as package authors/co-creators and copyright holders; Alexander Christensen remains a full author and copyright holder.
Added a formal AIGENIE software citation with the CRAN package DOI.
Updated the methodology citation to the published 2026 Behavior Research Methods article.

Anthropic Claude support: Generate items using Claude Sonnet 4.5, Opus 4, and Haiku 4.5 models via anthropic.API.
Jina AI embeddings: Compute embeddings using Jina models (v2, v3, v4) via jina.API, including Matryoshka truncation and task-specific adapters.
Slash-style Groq models: Route HuggingFace-style model IDs (e.g., "meta-llama/llama-4-scout-17b-16e-instruct", "qwen/qwen3-32b") to Groq when groq.API is provided.
chat() function: Send arbitrary prompts to any supported LLM provider with optional repetitions, temperature control, and system role customization.
list_available_models(): Query available models across all providers, with optional filtering by type ("chat" or "embedding").
Multi-provider mixing: Use one provider for item generation and another for embeddings (e.g., Anthropic items + Jina embeddings).

Comprehensive input validation across all user-facing functions.
Unified embedding dispatch via generate_embeddings() supporting OpenAI, Jina AI, HuggingFace (API and local), and sentence-transformers.
Model alias resolution for common shorthand names ("sonnet", "llama3", "deepseek", "qwen", etc.).
Improved error messages with provider-specific guidance.

Fixed provider detection for slash-style model names that were incorrectly routed to HuggingFace instead of Groq.

Complete rewrite of the pipeline architecture.
Introduced GENIE() for validation of user-provided item sets (embedding → EGA → UVA → bootEGA).
Added Groq API support for open-source LLM item generation.
Added local LLM support via llama-cpp-python (local_AIGENIE(), local_GENIE(), local_chat()).
Python environment management via UV and reticulate.
HuggingFace embedding support (API and local sentence-transformers).