'BERTopic'-Style Topic Modeling Without 'Python' R package Rhobots version 0.1.10 Implements the 'BERTopic' topic modeling pipeline directly in R: transformer-based sentence embedding, Uniform Manifold Approximation and Projection dimensionality reduction, Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering, and class-based term frequency-inverse document frequency topic extraction - all without any dependency on 'Python', 'conda', or 'reticulate'. Every stage runs in R through 'torch', 'safetensors', 'tok', 'uwot', and 'dbscan'. The package mirrors the accessor API of the original 'Python' package, adds integrated quality metrics and hyperparameter search tools, and introduces part-of-speech filtered and C-value-ranked representation models. Software Depends: R (>= 4.1.0) Imports: torch, safetensors, hfhub, tok, jsonlite, uwot, dbscan, Matrix, stats, wordpiece, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), plotly, httr2, gutenbergr, udpipe J.P.G. van der Pol <j.p.g.vanderpol@uu.nl> Comprehensive R Archive Network (CRAN) J.P.G. van der Pol [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=Rhobots/LICENSE) 2026-08-08 application/tgz https://CRAN.R-project.org/package=Rhobots doi:10.32614/CRAN.package.Rhobots