Package index • nalanda

All functions

aggregate_simulations(): Aggregate simulation runs
combine_book_files(): Combine chapter text files into one numbered file per book
combine_split_chapter_files(): Combine split chapter chunk files
compute_run_ai_metrics(): Compute derived pre/post effect metrics from raw turn-level output
compute_run_ai_metrics_cumulative(): Compute cumulative chapter metrics against the original baseline
compute_run_ai_metrics_one_turn(): Compute one-turn ingroup/outgroup metrics from raw output
evaluate_text_analysis(): Evaluate text-analysis outputs against reference labels
extract_pdf_text_with_llm(): Extract text from a PDF with a multimodal LLM
fix_text_file(): Fix text file encoding and normalize punctuation/whitespace
interpolate_spotify_audiobook_duration(): Interpolate Spotify audiobook duration from text size
list_book_chapters(): List book chapter files inside a books directory
make_annotation_prompt(): Build a numeric-response prompt for text analysis
make_baseline_prompt(): Build the baseline (Turn 1) prompt
make_post_prompt(): Build the post-intervention (Turn 2) prompt
make_treatment_prompt() build_simulate_treatment_prompt(): Build a concrete prompt for simulate_treatment()
model_agreement(): Compute inter-model agreement
model_agreement_sensitivity(): Summarize model agreement across analysis levels
model_pairwise_cor(): Pairwise model correlations
model_rank_consistency(): Compare model-derived rankings
nalanda(): A Random Historical Fact About Nalanda University
pairwise_for_level(): Pairwise model correlations at a chosen analysis level
plot_chapter_scores_faceted(): Faceted plot of chapter scores
plot_chapter_trajectories(): Plot chapter trajectories by book
plot_chapters_over_time(): Plot chapters over time (multi-timepoint means)
plot_chapters_over_time_one_turn(): Plot chapter trajectories for one-turn simulations
plot_forest_books(): Create a forest plot of book-level polarization reduction effects
plot_model_agreement(): Plot inter-model agreement
plot_top_unit_heatmap(): Plot model-by-unit rank heatmap
plot_top_unit_pairs(): Plot paired subgroup ranks for top units
plot_top_units(): Plot units that rank consistently high across models
prepare_forest_books(): Prepare book-level data for forest plotting
rank_weighted(): Rank rows using a weighted rubric
read_book_texts(): Read book chapters into a nested list
rename_chapters(): Rename chapter text files in a folder to a sequential order
renumber_chapters_across_folders(): Renumber chapter files across ordered folders
run_ai_cumulative_chapters(): Run AI model on books with cumulative chapter context
run_ai_on_chapters(): Run AI model on book chapters and collect structured responses
run_ai_on_chapters_one_turn(): Run AI model on book chapters with a single prompt per simulation
run_text_analysis(): Run row-wise text analysis with a prompt template
save_forest_plot(): Save a forest plot to PNG and PDF formats
simulate_treatment(): Simulate a generic multi-turn treatment workflow
split_book_section_by_headings(): Split a book section into chapter files using known headings
summarize_chapter_scores(): Summarize simulated chapter scores
summarize_identity_adherence(): Summarize whether the model adopts the requested identity
summarize_identity_match_rates(): Summarize identity match rates by model
summarize_model_correlations(): Summarize pairwise model correlations
summarize_simulation_stability(): Summarize simulation stability across chapters
summarize_top_units(): Summarize units that rank consistently high across models
summarize_treatment_results(): Summarize generic treatment results
toy_run_ai_turns: Toy raw turn-level AI simulation output
toy_sim_results: Toy simulated chapter results