agglomerative_clustering
                        Agglomerative (hierarchical) clustering
apply_mmr               Refine topic representations with Maximal
                        Marginal Relevance (MMR)
build_dtm               Build a sparse document-term matrix from a
                        character vector
c_tf_idf                Class-based TF-IDF (c-TF-IDF) for cluster-level
                        topic terms
classify_texts          Classify or label texts with a fine-tuned
                        BERT-family model
cls_pool                CLS-token pooling: extract the CLS hidden state
                        as the sentence vector
cluster_docs            Fit a clustering model and return cluster
                        labels
compare_topics          Compare topic prevalence across groups
cvalue_representation   Construct a C-value representation model
cvalue_terms            Compute C-value scores for candidate multi-word
                        terms
dim_project             Project new data using a fitted
                        dimensionality-reduction model
dim_reduce              Fit a dimensionality-reduction model and return
                        the reduced matrix
embed_texts.api_embedder
                        Embed a vector of texts to a numeric matrix
embed_texts_cached      Compute or load document embeddings from a
                        cache file
find_topics             Find topics most similar to a search term
fit_bertopic            Fit a BERTopic-style topic model
fit_topics_over_time    Fit independent BERTopic models per time period
                        and align topics
get_document_info       Get document-level topic assignments as a data
                        frame
get_representative_docs
                        Get representative documents for one or all
                        topics
get_stopwords           Return the built-in stopword list for a
                        language
get_topic               Get term-score representation for a single
                        topic
get_topic_info          Get topic-level metadata as a data frame
get_topics              Get all topic-term representations
guided_fit_bertopic     Guided topic modeling with user-supplied seed
                        words
hdbscan_clustering      HDBSCAN clustering
hierarchical_topics     Build a hierarchical topic tree from a fitted
                        topic model
kmeans_clustering       K-means clustering
label_topics_llm        Label topics using a large language model
load_bert_weights       Load BERT weights from a checkpoint into a
                        constructed model
load_bertopic           Load a previously saved BERTopic model
load_cohere_embedder    Load a Cohere embedding model
load_embeddings         Load an embedding matrix from disk
load_hf_bert            Load a BERT-family model from HuggingFace for
                        use in R
load_hf_classifier      Load a fine-tuned BERT-family classifier from
                        HuggingFace
load_openai_embedder    Load an OpenAI embedding model
load_specter2           Load a SPECTER2 model with a task-specific
                        adapter
load_stopwords          Load a stopword list from a character vector,
                        data frame, or file
make_wordpiece_tokenizer
                        Create a WordPiece tokenizer for BERT models
                        that lack 'tokenizer.json'
mean_pool               Mean-pool token-level hidden states into a
                        sentence vector
merge_topics            Manually merge a set of topics into one
no_reduction            Skip dimensionality reduction (identity
                        pass-through)
pca_reduction           PCA dimensionality reduction
pos_dtm                 Build a POS-filtered document-term matrix
pos_representation      Construct a POS-based representation model
predict.bertopic_fit    Predict topics for new documents using a fitted
                        BERTopic model
print.bert_encoder      Print method for bert_encoder objects
print.bertopic_fit      Print method for bertopic_fit objects
print.bertopic_flow     Print method for bertopic_flow objects
print.hf_classifier     Print method for hf_classifier objects
print_topics            Pretty-print discovered topics
reduce_outliers         Reassign noise documents to the nearest real
                        topic
reduce_topics           Reduce the number of topics by iteratively
                        merging the most similar pair
rhobots_demo            Run a quick Rhobots demo using classic novels
                        from Project Gutenberg
rhobots_install         Check Rhobots system dependencies and print
                        setup instructions
save_bertopic           Save a fitted BERTopic model to disk
save_embeddings         Save an embedding matrix to disk
stability_analysis      Measure topic stability across multiple random
                        seeds
sweep_topics            Sweep BERTopic hyperparameters and compare
                        topic quality
topic_coherence         Compute lexical coherence for discovered topics
topic_quality           Evaluate topic quality for a fitted BERTopic
                        model
topics_over_time        Compute how topic representations change over
                        time
transform_bertopic      Predict topics for new documents (standalone
                        alias)
umap_reduction          UMAP dimensionality reduction
visualize_barchart      Bar charts of top terms per topic
visualize_comparison    Interactive heatmap of topic - group
                        associations
visualize_hierarchy     Visualise the hierarchical topic tree as a
                        dendrogram
visualize_quality       Visualise topic quality metrics
visualize_stability     Interactive heatmap of pairwise ARI scores
visualize_sweep         Visualise the results of a parameter sweep
visualize_topic_flow    Sankey diagram of topic flow across periods
visualize_topics        Visualise documents in topic space
visualize_topics_over_time
                        Visualise topic frequency over time
zero_shot_topics        Zero-shot topic modeling with user-defined
                        topic labels
