sunlm 0.1.3.post2

A social-science-friendly toolkit for OLS regression and SPSS-like ANOVA

sunlm

sunlm is a lightweight, social-science–friendly Python package for OLS regression and SPSS-like ANOVA / ANCOVA.
It is designed for researchers in communication, psychology, marketing, and related social sciences who want clear, interpretable statistical output without complex statistical coding.

sunlm emphasizes:

• R-style formula syntax
• SPSS-equivalent ANOVA (Type III SS, sum coding)
• Standardized coefficients and semi-partial R²
• Clean, APA-style tables
• Simple, top-level API (sunlm.reg, sunlm.aov)

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Installation

pip install sunlm

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Quick Start: Load Example Data

sunlm includes a built-in example dataset for immediate practice.

import sunlm

df = sunlm.data()
df.head()

personalization	involvement	price_sensitivity	brand_trust	age	purchase_intention

0 1 1 -0.740814 1.408369 51.311519 7.000000 1 0 1 0.072907 -0.608711 41.350777 5.122004 2 0 0 0.403086 -1.320603 27.877310 2.840572 3 1 0 1.471929 -0.669619 46.907773 3.497987 4 1 0 0.307384 1.264625 35.208771 4.472068

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Tutorial 1️⃣ OLS Regression

import sunlm

model = sunlm.reg(
    "purchase_intention ~ personalization * involvement + age",
    data=df
)

model.summary()

📊 OLS Regression Summary

                        Unstd. B Std. Err. (unstd) t-value Std. β Semi-partial R²         p-value

Intercept 2.284 0.473 4.829 0.000 (< .001) personalization 0.814 0.319 2.553 0.305 0.035 0.012 (< .05) involvement 0.609 0.177 3.443 0.363 0.064 0.001 (< .001) personalization:involvement 0.374 0.248 1.510 0.204 0.012 0.134 (n.s.) age 0.039 0.010 3.713 0.273 0.074 0.000 (< .001)

📈 Model Fit Statistics

N 100 R² 0.490 Adj. R² 0.469

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Tutorial 2️⃣ ANOVA / ANCOVA (SPSS-like)

import sunlm

model = sunlm.aov(
    "purchase_intention ~ C(personalization) * C(involvement) + age",
    data=df
)

model.summary()

Tests of Between-Subjects Effects

Dependent Variable: purchase_intention Source sum_sq df mean_sq F p-value Partial Eta Squared Corrected Model 91.322 6 15.220 16.409 0.000 (< .001) 0.514 Intercept 64.597 1 64.597 69.640 0.000 (< .001) 0.428 personalization (factor) 33.449 1 33.449 36.060 0.000 (< .001) 0.279 involvement (factor) 40.764 2 20.382 21.973 0.000 (< .001) 0.321 personalization × involvement 2.883 2 1.441 1.554 0.217 (n.s.) 0.032 age 11.819 1 11.819 12.742 0.001 (< .001) 0.120 Error 86.266 93 0.928
Total 2719.617 100
Corrected Total 177.588 99

R Squared = 0.514 (Adjusted R Squared = 0.483)

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Tutorial 3️⃣ Post Hoc Tests

Tukey HSD

model.TukeyHSD("involvement")

Tukey HSD for involvement (alpha=0.05)

group1 group2 meandiff Std. Error p-value lower upper 0 1 1.186 0.235 0.000 (< .001) 0.494 1.877 0 2 1.494 0.243 0.000 (< .001) 0.779 2.210 1 2 0.309 0.233 0.534 (n.s.) -0.377 0.995

Bonferroni

model.Bonferroni("involvement")

Bonferroni Pairwise Comparisons for involvement (alpha=0.05)

group1 group2 meandiff Std. Error t p-value lower upper 0 1 1.149 0.237 4.846 0.000 (< .001) 0.571 1.727 0 2 1.576 0.246 6.410 0.000 (< .001) 0.976 2.175 1 2 0.427 0.234 1.828 0.212 (n.s.) -0.142 0.996

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Top-level API Summary

sunlm.reg(formula, data)   # OLS regression
sunlm.aov(formula, data)   # ANOVA / ANCOVA
sunlm.data()               # Load example dataset

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Citation

If you use sunlm in academic research, please cite it as:

Kim, S.-W. (2025). sunlm (Version 0.1.3.post1) [Software]. https://pypi.org/project/sunlm/

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License

MIT License

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Author

Seonwoo Kim
Northern Arizona University

Designed for researchers who want SPSS-equivalent statistics with Python clarity.