# 'BERTopic'-Style Topic Modeling Without 'Python'

## Documentation for package ‘Rhobots’ version 0.1.10

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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
classify_texts | Classify or label texts with a fine-tuned BERT-family model
classify_texts.default | Classify or label texts with a fine-tuned BERT-family model
classify_texts.hf_classifier | 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
c_tf_idf | Class-based TF-IDF (c-TF-IDF) for cluster-level topic 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 | Embed a vector of texts to a numeric matrix
embed_texts.api_embedder | Embed a vector of texts to a numeric matrix
embed_texts.bert_encoder | Embed a vector of texts to a numeric matrix
embed_texts.default | 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_topics | Get all topic-term representations
get_topic_info | Get topic-level metadata as a data frame
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_bertopic | Load a previously saved BERTopic model
load_bert_weights | Load BERT weights from a checkpoint into a constructed 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.bertopic_fit | Print method for bertopic_fit objects
print.bertopic_flow | Print method for bertopic_flow objects
print.bert_encoder | Print method for bert_encoder 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
topics_over_time | Compute how topic representations change over time
topic_coherence | Compute lexical coherence for discovered topics
topic_quality | Evaluate topic quality for a fitted BERTopic model
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_topics | Visualise documents in topic space
visualize_topics_over_time | Visualise topic frequency over time
visualize_topic_flow | Sankey diagram of topic flow across periods
zero_shot_topics | Zero-shot topic modeling with user-defined topic labels