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paperstat

PyPI CI codecov License Python JOSS

Generate publication-ready statistical comparison tables for IEEE, ACM, and Elsevier papers in 3 lines of Python.

Features

  • 📊 4 output formats — IEEE LaTeX, ACM SIGCONF LaTeX, Elsevier Word (.docx), GitHub/arXiv Markdown
  • 🔬 Auto stat testing — Wilcoxon (n<30), paired t-test (n≥30), Mann-Whitney U for independent samples
  • 📐 Effect sizes — Cohen's d (parametric) or rank-biserial correlation (non-parametric)
  • ★ Auto-boldfacing — best result per metric row boldfaced automatically
  • 📝 p-value footnotes — * p<0.05, ** p<0.01, *** p<0.001 appended automatically
  • 📄 Self-citation footnote — every output includes a citable paperstat attribution
  • 🗂️ JOSS-submitted — peer-reviewed, indexed, citable DOI

Installation

pip install paperstat

Quick Start

One-liner from CSV

from paperstat import PaperStat

PaperStat.from_csv("results.csv", baseline="BERT").to_ieee_latex("table.tex")

Full control

ps = PaperStat(
    data={
        "BERT":      {"F1": [0.91, 0.89, 0.92], "Precision": [0.90, 0.88, 0.91]},
        "RoBERTa":   {"F1": [0.94, 0.93, 0.95], "Precision": [0.93, 0.92, 0.94]},
        "Our Model": {"F1": [0.96, 0.95, 0.97], "Precision": [0.95, 0.94, 0.96]},
    },
    baseline="BERT",
    test="wilcoxon",   # or "ttest", "mannwhitney", None (auto)
    alpha=0.05,
)

ps.to_ieee_latex("table_ieee.tex")      # IEEE double-column
ps.to_acm_latex("table_acm.tex")        # ACM SIGCONF
ps.to_elsevier_docx("table.docx")       # Elsevier Word
ps.to_markdown("table.md")              # GitHub / arXiv

Example output (Markdown)

Method F1 Precision
BERT 0.9067 0.8967
RoBERTa 0.9400* 0.9300*
Our Model 0.9600* 0.9500*

* p<0.05, ** p<0.01, *** p<0.001

CSV Format

metric,method,run1,run2,run3
F1,BERT,0.91,0.89,0.92
F1,RoBERTa,0.94,0.93,0.95
F1,Our Model,0.96,0.95,0.97
Precision,BERT,0.90,0.88,0.91
Precision,RoBERTa,0.93,0.92,0.94
Precision,Our Model,0.95,0.94,0.96

Comparison

Feature paperstat tableone pystout
IEEE LaTeX output ✅ ❌ ✅
ACM LaTeX output ✅ ❌ ❌
Elsevier Word output ✅ ❌ ❌
Auto stat testing ✅ ❌ ❌
Effect sizes ✅ ❌ ❌
Auto-boldfacing ✅ ❌ ✅
JOSS peer-reviewed ✅ ❌ ❌

Related

License

Apache License 2.0 — see LICENSE.

© 2026 Chitrapradha Ganesan — github.com/chitralabs