Man pages for bakaburg1/BaySREn
BaySREn. An R package to automatise citation collection and screening in Systematic Reviews. Based on Bayesian active machine learning
| add_cumulative | Add cumulative numbers of positive and negative matches to a... |
| add_negative_terms | Add negative terms to rules |
| analyse_grid_search | Analyse the results of a parameter grid search |
| BaySREn-package | A package to automatise citation collection and screening in... |
| clean_date_filter_arg | Transform a year range into the format required by a specific... |
| clean_record_textfields | Clean up problematic text in the citation data. |
| coalesce_labels | Join classification labels into one |
| compute_BART_model | Use Bayesian Additive Regression Trees to predict records... |
| compute_changes | Describe changes in the record labels after a... |
| consolidate_results | Include manual review in annotation result summaries |
| create_annotation_file | Create an annotation data set ready for relevance... |
| create_session | Create a Session starting from an annotation data set. |
| create_training_set | Create the training set for the automatic classification |
| DTM.add_ngrams | Find non-consecutive n-grams |
| DTM.aggr_synonyms | Aggregate redundant terms |
| enrich_annotation_file | Enrich an Annotation data set with predictions based on a... |
| estimate_performance | Evaluate and report classification performance of given an... |
| estimate_positivity_rate_model | Train a simple Bayesian logistic model on classification... |
| extract_rules | Extract screening rules from an Annotation data set |
| extract_source_file_paths | Extract the path to citation records files |
| extract_var_imp | Extract the importance of features in the Document Term... |
| fix_duplicated_records | Resolve duplicated records in a record data frame |
| format_interval | Format a 3-values statistic |
| format_performance | Pretty formatting of Session performance analysis |
| format_var_imp | Format variable importance results |
| generate_rule_selection_set | Generate a rule selection set for user review |
| get_session_files | Retrieve the path of the resources linked to a session. |
| get_source_distribution | Distribution of the number of sources in common per record |
| import_classification | Import classifications from a previously labelled annotation... |
| import_data | Read a data file/object |
| join_records | Join citation data frames and resolve record duplication |
| lemmatize | Transform words into their base form |
| order_by_query_match | Reorder a data frame of records according to simple query... |
| parse_embase | Parse EMBASE raw data |
| parse_ieee | Parse IEEE raw data |
| parse_pubmed | Parse Pubmed raw data |
| parse_scopus | Parse SCOPUS raw data |
| parse_wos | Parse Web of Science raw data |
| perform_grid_evaluation | Perform a grid evaluation of parameters to tune the... |
| perform_search_session | Wrapper function to acquire citation data from multiple... |
| plot_classification_trend | Plot the cumulative trend of positive and negative labelled... |
| plot_predictive_densities | Plot posterior predictive distributions generated by the... |
| print_table | Publication friendly tables for .rmd files |
| read_bib_files | Import and parse citation data files |
| rules_to_query | Transform a rule set in a search query |
| search_ieee | Automatic search on IEEE database |
| search_pubmed | Automatic search on Pubmed database |
| search_wos | Automatic search on Web Of Science database |
| simplify_ruleset | Remove redundant rules and rule components |
| source_session_summary_to_list | Format records' source distribution as a list |
| summarise_annotations | Describe results of a Classification/Review session |
| summarise_annotations_by_session | Describe results of all Classification/Review sessions |
| summarise_by_source | Record distribution between sources in an Annotation file |
| summarise_sources_by_session | Record distribution between sources for each session |
| summarise_vector | Summarise a discrete vector |
| text_to_DTM | Convert a vector of text documents into a Document Term... |
| tokenize_authors | Tokenize authors in records |
| tokenize_keywords | Tokenize keywords in records |
| tokenize_MESH | Tokenize MESH keywords in records |
| tokenize_text | Clean up text into tokens |
