Access to Large Language Model Predictions R package pangoling version 1.0.3 Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem <https://huggingface.co/>. The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) <https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, for details on BERT and masked models, see Devlin et al. (2019) <doi:10.48550/arXiv.1810.04805>. By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP). Software Depends: R (>= 4.1.0) Imports: cachem, data.table, memoise, reticulate, rstudioapi, stats, tidyselect, tidytable (>= 0.7.2), utils Suggests: brms, knitr, parallel, rmarkdown, spelling, testthat (>= 3.0.0), tictoc, covr Bruno Nicenboim <b.nicenboim@tilburguniversity.edu> Comprehensive R Archive Network (CRAN) Bruno Nicenboim [aut, cre] (ORCID: <https://orcid.org/0000-0002-5176-3943>), Chris Emmerly [ctb], Giovanni Cassani [ctb], Lisa Levinson [rev], Utku Turk [rev] MIT + file LICENSE (https://CRAN.R-project.org/package=pangoling/LICENSE) 2025-04-07 application/tgz https://CRAN.R-project.org/package=pangoling doi:10.32614/CRAN.package.pangoling en-US