<jupyter_start><jupyter_text>Effective sample size (R):::{include} ess.md:start-after: intro-start :end-before: intro-end:::<jupyter_code>library("posterior")
eight_schools_array <- example_draws("eight_schools")
ess_bulk(eight_schools_array)<jupyter_output>This is posterior version 0.1.3<jupyter_text>:::{include} ess.md:start-after: tail-note-start:end-before: tail-note-end:::<jupyter_code>ess_tail(eight_schools_array)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1545253863134658, 'fraction_numerical': 0.006622516556291391, 'mean_word_length': 12.352941176470589, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '71282', 'license': 'Apache-2.0', 'n_tokens_mistral': 179, 'n_tokens_neox': 173, 'n_words': 18, 'path': 'diagnostics/ess-R.ipynb', 'repo_name': 'arviz-devs/bayesian-workflow', 'size': '453'}