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This is a CRAN-maintenance release. 1.3.1 adds no new user-facing
features; it fixes two issues surfaced by R CMD check on
CRAN incoming and tightens packaging metadata so the package clears
incoming feasibility checks.
aggregate import:
rate_openai_weighted() calls
stats::aggregate() for mean/SD pooling. CRAN incoming
flagged this as an undefined global function. Added
importFrom(stats, aggregate) to the NAMESPACE
(generated via roxygen tag).rank parameter
documentation: alignment() accepts a
rank argument (logical, default FALSE) but it
was missing from the roxygen \usage{} block, triggering a
Codoc mismatches WARNING. Documented rank in
man/alignment.Rd (added to \usage{} and
\arguments{} with
@param rank Logical; ifTRUE, compute Spearman/Sigma rank correlation alongside Pearson statistics. DefaultFALSE.).Citation tightening:
inst/CITATION is now a single APA 7 entry pointing at the
PsyArXiv preprint (doi:10.31234/osf.io/mje6w_v1). The
duplicate “software manual” entry was removed —
citation("chatRater") now returns one tidy reference.Date field: Updated Date:
in DESCRIPTION to 2026-06-25 to match the
build date.R CMD check is now clean on CRAN incoming (Windows
Server 2022, R-devel r90190, ucrt):
Status: OK
No new warnings, no notes, no missing documentation entries.
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}).rate_openai_weighted() bypasses llmcoder to
make a direct httr2 call with logprobs = TRUE,
enabling fine-grained probability extraction without modifying the
llmcoder dependency.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).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.include_probs = TRUEgenerate_ratings_for_all()@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.curl::form_file() usage in Whisper API upload
(previously used incorrect httr2 multipart syntax).debug parameter not passed to
rate_build_content_blocks(), causing
"argument is not interpretable as logical" error when
processing audio or image stimuli.inst/CITATION with APA 7-formatted entries for
both the software (Manual) and the associated PsyArXiv preprint
(doi:10.31234/osf.io/mje6w_v1).
citation("chatRater") returns both references.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.generate_ratings() and
generate_ratings_for_all() are now exported.openai package).generate_ratings() for single-stimulus rating via
LLM.generate_ratings_for_all() for batch rating of multiple
stimuli.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."1-7",
"0-10", etc.).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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