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  "Package": "Rhobots",
  "Title": "'BERTopic'-Style Topic Modeling Without 'Python'",
  "Version": "0.1.10",
  "Authors@R": "person(\"J.P.G.\", \"van der Pol\", , \"j.p.g.vanderpol@uu.nl\", role = c(\"aut\", \"cre\"))",
  "Description": "Implements the 'BERTopic' topic modeling pipeline directly\nin R: transformer-based sentence embedding, Uniform Manifold\nApproximation and Projection dimensionality reduction,\nHierarchical Density-Based Spatial Clustering of Applications\nwith Noise clustering, and class-based term frequency-inverse\ndocument frequency topic extraction - all without any\ndependency on 'Python', 'conda', or 'reticulate'. Every stage\nruns in R through 'torch', 'safetensors', 'tok', 'uwot', and\n'dbscan'. The package mirrors the accessor API of the original\n'Python' package, adds integrated quality metrics and\nhyperparameter search tools, and introduces part-of-speech\nfiltered and C-value-ranked representation models.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/JPvdP/RhoBots",
  "BugReports": "https://github.com/JPvdP/RhoBots/issues",
  "SystemRequirements": "Microsoft Visual C++ Redistributable 2022 (Windows\nonly; required by torch)",
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  "Repository": "https://jpvdp.r-universe.dev",
  "Date/Publication": "2026-10-08 09:32:32 UTC",
  "RemoteUrl": "https://github.com/jpvdp/rhobots",
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  "Author": "J.P.G. van der Pol [aut, cre]",
  "Maintainer": "J.P.G. van der Pol <j.p.g.vanderpol@uu.nl>",
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  "_created": "2026-10-08T10:45:18.000Z",
  "_published": "2026-10-08T11:33:03.481Z",
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    "author": "j.p.g.vanderpol@uu.nl <j.p.g.vanderpol@uu.nl>",
    "committer": "j.p.g.vanderpol@uu.nl <j.p.g.vanderpol@uu.nl>",
    "message": "Merge branch 'embed-memory': reduce embed_texts() memory use; fix chunked padding and HDBSCAN single-cluster bugs\n\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n",
    "time": 1791451952
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    {
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  "_tags": [],
  "_topics": [
    "data-analysis",
    "natural-language-processing",
    "topic-modeling",
    "cpp"
  ],
  "_stars": 8,
  "_contributors": [
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  "_exports": [
    "agglomerative_clustering",
    "apply_mmr",
    "build_dtm",
    "c_tf_idf",
    "classify_texts",
    "cls_pool",
    "cluster_docs",
    "compare_topics",
    "cvalue_representation",
    "cvalue_terms",
    "dim_project",
    "dim_reduce",
    "embed_texts",
    "embed_texts_cached",
    "find_topics",
    "fit_bertopic",
    "fit_topics_over_time",
    "get_document_info",
    "get_representative_docs",
    "get_stopwords",
    "get_topic",
    "get_topic_info",
    "get_topics",
    "guided_fit_bertopic",
    "hdbscan_clustering",
    "hierarchical_topics",
    "kmeans_clustering",
    "label_topics_llm",
    "load_bert_weights",
    "load_bertopic",
    "load_cohere_embedder",
    "load_embeddings",
    "load_hf_bert",
    "load_hf_classifier",
    "load_openai_embedder",
    "load_specter2",
    "load_stopwords",
    "make_wordpiece_tokenizer",
    "mean_pool",
    "merge_topics",
    "no_reduction",
    "pca_reduction",
    "pos_dtm",
    "pos_representation",
    "print_topics",
    "reduce_outliers",
    "reduce_topics",
    "rhobots_demo",
    "rhobots_install",
    "save_bertopic",
    "save_embeddings",
    "stability_analysis",
    "sweep_topics",
    "topic_coherence",
    "topic_quality",
    "topics_over_time",
    "transform_bertopic",
    "umap_reduction",
    "visualize_barchart",
    "visualize_comparison",
    "visualize_hierarchy",
    "visualize_quality",
    "visualize_stability",
    "visualize_sweep",
    "visualize_topic_flow",
    "visualize_topics",
    "visualize_topics_over_time",
    "zero_shot_topics"
  ],
  "_help": [
    {
      "page": "agglomerative_clustering",
      "title": "Agglomerative (hierarchical) clustering",
      "topics": [
        "agglomerative_clustering"
      ]
    },
    {
      "page": "apply_mmr",
      "title": "Refine topic representations with Maximal Marginal Relevance (MMR)",
      "topics": [
        "apply_mmr"
      ]
    },
    {
      "page": "build_dtm",
      "title": "Build a sparse document-term matrix from a character vector",
      "topics": [
        "build_dtm"
      ]
    },
    {
      "page": "c_tf_idf",
      "title": "Class-based TF-IDF (c-TF-IDF) for cluster-level topic terms",
      "topics": [
        "c_tf_idf"
      ]
    },
    {
      "page": "classify_texts",
      "title": "Classify or label texts with a fine-tuned BERT-family model",
      "topics": [
        "classify_texts",
        "classify_texts.default",
        "classify_texts.hf_classifier"
      ]
    },
    {
      "page": "cls_pool",
      "title": "CLS-token pooling: extract the CLS hidden state as the sentence vector",
      "topics": [
        "cls_pool"
      ]
    },
    {
      "page": "cluster_docs",
      "title": "Fit a clustering model and return cluster labels",
      "topics": [
        "cluster_docs"
      ]
    },
    {
      "page": "compare_topics",
      "title": "Compare topic prevalence across groups",
      "topics": [
        "compare_topics"
      ]
    },
    {
      "page": "cvalue_representation",
      "title": "Construct a C-value representation model",
      "topics": [
        "cvalue_representation"
      ]
    },
    {
      "page": "cvalue_terms",
      "title": "Compute C-value scores for candidate multi-word terms",
      "topics": [
        "cvalue_terms"
      ]
    },
    {
      "page": "dim_project",
      "title": "Project new data using a fitted dimensionality-reduction model",
      "topics": [
        "dim_project"
      ]
    },
    {
      "page": "dim_reduce",
      "title": "Fit a dimensionality-reduction model and return the reduced matrix",
      "topics": [
        "dim_reduce"
      ]
    },
    {
      "page": "embed_texts_cached",
      "title": "Compute or load document embeddings from a cache file",
      "topics": [
        "embed_texts_cached"
      ]
    },
    {
      "page": "embed_texts",
      "title": "Embed a vector of texts to a numeric matrix",
      "topics": [
        "embed_texts",
        "embed_texts.api_embedder",
        "embed_texts.bert_encoder",
        "embed_texts.default"
      ]
    },
    {
      "page": "find_topics",
      "title": "Find topics most similar to a search term",
      "topics": [
        "find_topics"
      ]
    },
    {
      "page": "fit_bertopic",
      "title": "Fit a BERTopic-style topic model",
      "topics": [
        "fit_bertopic"
      ]
    },
    {
      "page": "fit_topics_over_time",
      "title": "Fit independent BERTopic models per time period and align topics",
      "topics": [
        "fit_topics_over_time"
      ]
    },
    {
      "page": "get_document_info",
      "title": "Get document-level topic assignments as a data frame",
      "topics": [
        "get_document_info"
      ]
    },
    {
      "page": "get_representative_docs",
      "title": "Get representative documents for one or all topics",
      "topics": [
        "get_representative_docs"
      ]
    },
    {
      "page": "get_stopwords",
      "title": "Return the built-in stopword list for a language",
      "topics": [
        "get_stopwords"
      ]
    },
    {
      "page": "get_topic",
      "title": "Get term-score representation for a single topic",
      "topics": [
        "get_topic"
      ]
    },
    {
      "page": "get_topic_info",
      "title": "Get topic-level metadata as a data frame",
      "topics": [
        "get_topic_info"
      ]
    },
    {
      "page": "get_topics",
      "title": "Get all topic-term representations",
      "topics": [
        "get_topics"
      ]
    },
    {
      "page": "guided_fit_bertopic",
      "title": "Guided topic modeling with user-supplied seed words",
      "topics": [
        "guided_fit_bertopic"
      ]
    },
    {
      "page": "hdbscan_clustering",
      "title": "HDBSCAN clustering",
      "topics": [
        "hdbscan_clustering"
      ]
    },
    {
      "page": "hierarchical_topics",
      "title": "Build a hierarchical topic tree from a fitted topic model",
      "topics": [
        "hierarchical_topics"
      ]
    },
    {
      "page": "kmeans_clustering",
      "title": "K-means clustering",
      "topics": [
        "kmeans_clustering"
      ]
    },
    {
      "page": "label_topics_llm",
      "title": "Label topics using a large language model",
      "topics": [
        "label_topics_llm"
      ]
    },
    {
      "page": "load_bert_weights",
      "title": "Load BERT weights from a checkpoint into a constructed model",
      "topics": [
        "load_bert_weights"
      ]
    },
    {
      "page": "load_bertopic",
      "title": "Load a previously saved BERTopic model",
      "topics": [
        "load_bertopic"
      ]
    },
    {
      "page": "load_cohere_embedder",
      "title": "Load a Cohere embedding model",
      "topics": [
        "load_cohere_embedder"
      ]
    },
    {
      "page": "load_embeddings",
      "title": "Load an embedding matrix from disk",
      "topics": [
        "load_embeddings"
      ]
    },
    {
      "page": "load_hf_bert",
      "title": "Load a BERT-family model from HuggingFace for use in R",
      "topics": [
        "load_hf_bert"
      ]
    },
    {
      "page": "load_hf_classifier",
      "title": "Load a fine-tuned BERT-family classifier from HuggingFace",
      "topics": [
        "load_hf_classifier"
      ]
    },
    {
      "page": "load_openai_embedder",
      "title": "Load an OpenAI embedding model",
      "topics": [
        "load_openai_embedder"
      ]
    },
    {
      "page": "load_specter2",
      "title": "Load a SPECTER2 model with a task-specific adapter",
      "topics": [
        "load_specter2"
      ]
    },
    {
      "page": "load_stopwords",
      "title": "Load a stopword list from a character vector, data frame, or file",
      "topics": [
        "load_stopwords"
      ]
    },
    {
      "page": "make_wordpiece_tokenizer",
      "title": "Create a WordPiece tokenizer for BERT models that lack 'tokenizer.json'",
      "topics": [
        "make_wordpiece_tokenizer"
      ]
    },
    {
      "page": "mean_pool",
      "title": "Mean-pool token-level hidden states into a sentence vector",
      "topics": [
        "mean_pool"
      ]
    },
    {
      "page": "merge_topics",
      "title": "Manually merge a set of topics into one",
      "topics": [
        "merge_topics"
      ]
    },
    {
      "page": "no_reduction",
      "title": "Skip dimensionality reduction (identity pass-through)",
      "topics": [
        "no_reduction"
      ]
    },
    {
      "page": "pca_reduction",
      "title": "PCA dimensionality reduction",
      "topics": [
        "pca_reduction"
      ]
    },
    {
      "page": "pos_dtm",
      "title": "Build a POS-filtered document-term matrix",
      "topics": [
        "pos_dtm"
      ]
    },
    {
      "page": "pos_representation",
      "title": "Construct a POS-based representation model",
      "topics": [
        "pos_representation"
      ]
    },
    {
      "page": "predict.bertopic_fit",
      "title": "Predict topics for new documents using a fitted BERTopic model",
      "topics": [
        "predict.bertopic_fit"
      ]
    },
    {
      "page": "print_topics",
      "title": "Pretty-print discovered topics",
      "topics": [
        "print_topics"
      ]
    },
    {
      "page": "print.bert_encoder",
      "title": "Print method for bert_encoder objects",
      "topics": [
        "print.bert_encoder"
      ]
    },
    {
      "page": "print.bertopic_fit",
      "title": "Print method for bertopic_fit objects",
      "topics": [
        "print.bertopic_fit"
      ]
    },
    {
      "page": "print.bertopic_flow",
      "title": "Print method for bertopic_flow objects",
      "topics": [
        "print.bertopic_flow"
      ]
    },
    {
      "page": "print.hf_classifier",
      "title": "Print method for hf_classifier objects",
      "topics": [
        "print.hf_classifier"
      ]
    },
    {
      "page": "reduce_outliers",
      "title": "Reassign noise documents to the nearest real topic",
      "topics": [
        "reduce_outliers"
      ]
    },
    {
      "page": "reduce_topics",
      "title": "Reduce the number of topics by iteratively merging the most similar pair",
      "topics": [
        "reduce_topics"
      ]
    },
    {
      "page": "rhobots_demo",
      "title": "Run a quick Rhobots demo using classic novels from Project Gutenberg",
      "topics": [
        "rhobots_demo"
      ]
    },
    {
      "page": "rhobots_install",
      "title": "Check Rhobots system dependencies and print setup instructions",
      "topics": [
        "rhobots_install"
      ]
    },
    {
      "page": "save_bertopic",
      "title": "Save a fitted BERTopic model to disk",
      "topics": [
        "save_bertopic"
      ]
    },
    {
      "page": "save_embeddings",
      "title": "Save an embedding matrix to disk",
      "topics": [
        "save_embeddings"
      ]
    },
    {
      "page": "stability_analysis",
      "title": "Measure topic stability across multiple random seeds",
      "topics": [
        "stability_analysis"
      ]
    },
    {
      "page": "sweep_topics",
      "title": "Sweep BERTopic hyperparameters and compare topic quality",
      "topics": [
        "sweep_topics"
      ]
    },
    {
      "page": "topic_coherence",
      "title": "Compute lexical coherence for discovered topics",
      "topics": [
        "topic_coherence"
      ]
    },
    {
      "page": "topic_quality",
      "title": "Evaluate topic quality for a fitted BERTopic model",
      "topics": [
        "topic_quality"
      ]
    },
    {
      "page": "topics_over_time",
      "title": "Compute how topic representations change over time",
      "topics": [
        "topics_over_time"
      ]
    },
    {
      "page": "transform_bertopic",
      "title": "Predict topics for new documents (standalone alias)",
      "topics": [
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