Changes in version 1.3.1                        

Major Changes

  - Probability-weighted scoring (method = "weighted"): New rating
    method following Brysbaert et al. (2025). Instead of extracting the
    most probable token, the function retrieves log probabilities for
    all candidate rating tokens and computes a continuous score as
    Σ(rating × p(rating)). This yields finer-grained estimates than
    dominant-token decoding alone. Supported for provider = "openai"
    with text stimuli only.
  - top_logprobs parameter (1-20): Controls how many most-likely tokens
    are returned at each position. Default 5; set to 20 for maximum
    coverage of token surface variants (e.g., "3", " 3", "3.").
  - include_probs parameter: When TRUE, the returned data frame includes
    a probs_json column with the full probability distribution over
    rating values as a JSON string (e.g., {"3":0.52,"4":0.43,"2":0.05}).
  - Direct API call for weighted mode: rate_openai_weighted() bypasses
    llmcoder to make a direct httr2 call with logprobs = TRUE, enabling
    fine-grained probability extraction without modifying the llmcoder
    dependency.

New Examples

  - Weighted rating with include_probs = TRUE
  - Batch weighted rating with generate_ratings_for_all()

Major Changes

  - Migrated from openai to llmcoder: Core rating for text stimuli now
    uses llmcoder::call_llm(), enabling multi-provider support (OpenAI,
    Anthropic, Ollama, LM Studio, DeepSeek, Groq, Mistral, OpenRouter,
    OpenAI-compatible).
  - Local image file support: Images can now be passed as local file
    paths (base64-encoded) in addition to URLs. Vision-capable models
    (gpt-4o, etc.) are used automatically for image stimuli.
  - Audio support: Local audio files (mp3, wav, ogg, flac, m4a, aac) are
    now supported; transcribed via OpenAI Whisper before rating.
  - return_type parameter: Control output format: "numeric" (default,
    extract numbers only), "text" (return full text response), "raw"
    (return unprocessed API response).
  - columns parameter (experimental): Select which columns appear in the
    returned data frame. Available columns: "stim", "rating",
    "iteration", "scale", "type", "provider", "model". Default NULL
    returns all columns.

Bug Fixes

  - Fixed curl::form_file() usage in Whisper API upload (previously used
    incorrect httr2 multipart syntax).
  - Fixed debug parameter not passed to rate_build_content_blocks(),
    causing "argument is not interpretable as logical" error when
    processing audio or image stimuli.

Examples

  - Expanded @examples in generate_ratings() and
    generate_ratings_for_all(): 9 detailed examples covering text
    (string), image (local file + URL), and audio (local file) stimuli,
    with both numeric rating and full-text description workflows.

Dependencies

  - Imports: llmcoder (>= 1.2.0), base64enc, tools, httr2, curl,
    jsonlite

                 Changes in version 1.2.0 (2025-08-18)                  

Changes

  - Removed text analysis utilities: get_lexical_coverage(),
    get_word_frequency(), get_zipf_metric(), get_levenshtein_d(),
    get_semantic_transparency() have been removed to focus the package
    on its core rating functionality.
  - Simplified API: Only generate_ratings() and
    generate_ratings_for_all() are now exported.
  - Image URL support: Stimuli can now be image URLs (passed to GPT-4
    Vision via the openai package).

Dependencies

  - Imports: openai, tools

                 Changes in version 1.1.0 (2025-03-03)                  

Initial CRAN Release

  - generate_ratings() for single-stimulus rating via LLM.
  - generate_ratings_for_all() for batch rating of multiple stimuli.
  - Text analysis utilities:
      - get_lexical_coverage(): Calculate lexical coverage of a text.
      - get_word_frequency(): Look up word frequency information.
      - get_zipf_metric(): Calculate Zipf metric for a text.
      - get_levenshtein_d(): Calculate Levenshtein distance between
        strings.
      - get_semantic_transparency(): Rate semantic transparency.
  - OpenAI API integration (text-only stimuli).
  - Rating scale validation (format: "1-7", "0-10", etc.).