Man pages for agoldst/dfrtopics
Tools for exploring topic models of text
| alignment_frame | Organize alignment results for inspection |
| align_topics | Align topics across models |
| browser_alignment_table | Generate an HTML table from a topic clustering |
| calc_imi | IMI calculation routine |
| cite_articles | Generate simple citation strings from metadata |
| compatible_instances | Generate a new InstanceList compatible with an old one |
| dfr_browser | Create and launch a model browser |
| dfr_filename_id | Convert wordcount filenames to JSTOR document id's |
| dfr_id_url | Convert a DfR ID into a JSTOR URL |
| dfrtopics | Explore and analyze topic models |
| doc_ids | Retrieve document IDs |
| docs_top_topics | Top-ranked topics for documents |
| doc_topics | The document-topic matrix |
| dt_smooth_normalize | Normalizing the document-topic matrix |
| entropy | Entropy of a vector |
| export_browser_data | Output data files for dfr-browser |
| export_browser_doc_topics | Export document-topic file for dfr-browser |
| export_browser_info | Export browser information/configuration for dfr-browser |
| export_browser_metadata | Export metadata file for dfr-browser |
| export_browser_topic_scaled | Export scaled topic coordinates for dfr-browser |
| export_browser_topic_words | Export topic-word file for dfr-browser |
| foreign_model | Glue for results from other topic-modeling packages |
| gather_matrix | Transform a matrix into a long ("tidy") data frame |
| get_instance | Retrieve an instance from the instance list by id |
| hyperparameters | Retrieve estimated model hyperparameters |
| id_dfr_filename | Convert JSTOR document id's to 'wordcounts*.CSV' filenames |
| imi_check | Posterior predictive checking for individual words |
| imi_simulate | Simulated IMI values |
| imi_topic | Instantaneous mutual information of words and documents in a... |
| inferencer | Get a topic inferencer object |
| infer_topics | Infer document topics |
| instances | Access the InstanceList stored by a model |
| instances_ids | Extract document id's from an InstanceList |
| instances_lengths | Retrieve instance lengths |
| instances_Matrix | Extract term-document matrix from instances |
| instances_vocabulary | Retrieve the vocabulary from the instances |
| instance_text | Transform an instance back into text |
| instance_vector | Convert a MALLET Instance to an integer vector |
| JS_divergence | Distance measures for topics |
| load_doc_topics | Load model elements into a model object |
| load_from_mallet_state | Load model from MALLET state output |
| load_mallet_model | Read in model outputs from files |
| load_mallet_model_directory | Load a model with conventional filenames from a directory |
| load_mallet_model_legacy | Load a model with files from dfrtopics 0.1 |
| load_sampling_state | Load Gibbs sampling state into model object |
| make_instances | Create MALLET instances from a document frame |
| mallet-logging | MALLET logging options |
| mallet_model | The model object |
| mallet_model_inferred | An inferred topic model of new documents |
| merge.mallet_model | Combine a topic model with inferred document topics |
| metadata | Retrieve metadata |
| mi_check | Posterior predictive checking for topics |
| mi_simulate | Simulated MI values |
| mi_topic | Mutual information of words and documents in a topic |
| model_dfr_documents | Make a topic model of DfR documents |
| model_distances | Calculate topic dissimilarity across models |
| modeling_parameters | Access stored modeling parameters |
| n_docs | Access the number of documents modeled |
| normalize_cols | Normalize columns to sum to one |
| normalize_rows | Normalize rows to sum to one |
| n_topics | Access the number of topics in the model |
| ParallelTopicModel | Access MALLET's model object |
| pipe | Pipe operator |
| plot_imi_check | Visualize IMI scores for the top words in topics |
| plot_mi_check | Visualize a topic's MI score in comparison to simulated... |
| plot_series | Plot time series of yearly topic proportions as bars |
| plot_topic_scaled | Plot topics on the plane |
| plot_top_words | Plot a single topic's most probable words |
| plot_word_topic_series | Plot word frequencies within topics over time |
| pubdate_Date | Convert JSTOR pubdate strings to Date objects |
| read_dfr_citations | Read a single 'citations.CSV' or 'citations.tsv' file. |
| read_dfr_metadata | Make a dataframe from 'citations.CSV' or 'citations.tsv'... |
| read_diagnostics | Read MALLET model-diagnostic results. |
| read_inferencer | Retrieve an inferencer object from a file |
| read_instances | Read a mallet 'InstanceList' object from a file |
| read_matrix_csv | Read in a numeric matrix |
| read_sampling_state | Read in a Gibbs sampling state |
| read_wordcounts | Convert DfR wordcount files to a long-format data frame |
| rescale_cols | Rescale the columns of a matrix |
| rescale_rows | Rescale the rows of a matrix |
| rmultinom_sparse | Draw from multinomial distributions |
| row_dists | Measure matrix row distances |
| row_entropies | Entropy of sparse-matrix rows |
| RTopicModel | Access MALLET's glue model object |
| simplify_state | Reduce a MALLET sampling state on disk to a simplified form |
| sum_row_groups | Sum ragged groups of matrix rows or columns |
| tdm_topic | The term-document matrix for a topic |
| tidy_check | Tidy results of posterior checks |
| top_docs | Top-ranked documents in topics |
| topic_divergences | Topic distance functions |
| topic_docs_word | The topic-document matrix for a specific word |
| topic_labels | Quick shorthands for topics |
| topic_report | Overview visualization of multiple topics |
| topic_scaled_2d | Scaled topic coordinates in 2D space |
| topic_series | Topic time series |
| topic_words | The topic-words matrix |
| top_n_row | Utility functions for finding top-ranking row/column elements |
| top_words | Topic key words |
| train_model | Train a topic model |
| tw_smooth_normalize | Scoring methods for words in topics |
| vocabulary | Retrieve model corpus vocabulary |
| widths | Aligned-topic cluster widths |
| wordcounts_doc_lengths | Calculate document lengths |
| wordcounts_DocumentTermMatrix | Convert word counts data frame to a DocumentTermMatrix |
| wordcounts_instances | Create MALLET instances from a word-counts data frame |
| wordcounts_Matrix | Convert a word-counts data frame into document-term matrix |
| wordcounts_remove_rare | Remove infrequent words |
| wordcounts_remove_stopwords | Remove stopwords from a wordcounts data frame |
| wordcounts_stm_inputs | Convert word counts data frame to Structural Topic Model... |
| wordcounts_texts | Convert long-format word-counts into documents |
| wordcounts_word_totals | Calculate total corpus-wide word counts |
| word_ids | Get numeric word indices |
| word_series_matrix | Aggregate (word/topic) counts by time period |
| words_top_topics | Top-ranked topics for documents |
| write_diagnostics | Save MALLET's topic model diagnostics as XML |
| write_inferencer | Save an inferencer object to a file |
| write_instances | Save a mallet InstanceList object to a file |
| write_mallet_model | A convenience function for saving all the model outputs at... |
| write_mallet_state | Save the Gibbs sampling state to a file |
| write_matrix_csv | Write out a numeric matrix to a text file |
