OLSengine: Transparent Linear and Causal Inference Models 🚀

OLSengine is an open-source R package designed for applied researchers in social sciences. It provides a comprehensive, zero-dependency mathematical engine for fundamental statistical methods and modern causal inference techniques.

Built under the philosophy of “Assisted Simplicity”, OLSengine acts as a methodological customs filter (“Aduana”). It unifies model estimation and diagnostics in a single step, alerting researchers to violations of mathematical assumptions and guiding them toward robust alternatives without making automatic decisions behind their backs.

🌟 Core Features

• Six Estimation Engines: OLS regression, ANOVA/t-tests, logistic regression, panel data (FE/RE), instrumental variables (2SLS), and difference-in-differences (DiD).
• Zero External Dependencies: Built entirely on pure Base R matrix algebra and native stats functions for maximum long-term stability and algorithmic transparency.
• The Methodological Customs (Aduana): Automatically runs background diagnostics (Breusch-Pagan, Shapiro-Wilk, Levene, VIF, Hosmer-Lemeshow, Hausman, weak instruments, parallel trends) and outputs actionable, literature-backed warnings.
• Paper-Ready Outputs: Returns hierarchical tables formatted for direct inclusion in academic manuscripts (APA style), automatically calculating effect sizes and exact p-values.
• Publication-Ready Plots: Generates APA-style, grayscale plots (forest plots, group means, logistic curves, panel trends, DiD parallel trends) without requiring ggplot2.

📦 Installation

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("msoto-perez/OLSengine")

🛠️ Quick Start

The package revolves around a single, powerful wrapper function: paper_engine().

1. OLS Regression (with Robust SE)

Detects heteroskedasticity and multicollinearity. HC3 robust standard errors available.

library(OLSengine)

# Standard OLS
model_ols <- paper_engine(y ~ x1 + x2, data = my_data, model = "ols")

# With HC3 Robust Standard Errors
model_robust <- paper_engine(y ~ x1 + x2, data = my_data, model = "ols", robust = TRUE)

# View results
model_robust$tables$Table2_OLS_Estimation
model_robust$messages

# Generate forest plot
plot_engine(model_robust)

2. ANOVA / t-tests (4-Way Engine)

Handles independent or paired designs, parametric and non-parametric tests.

# Auto-pilot: switches to non-parametric if normality fails
model_anova <- paper_engine(score ~ group, data = experiment_data, 
                            model = "anova", non_parametric = "auto")

# Generate group means plot with 95% CI
plot_engine(model_anova)

3. Logistic Regression

Reports Odds Ratios, McFadden’s and Nagelkerke’s Pseudo R², classification accuracy.

model_logit <- paper_engine(purchased ~ age + income, data = consumer_data, 
                            model = "logit")

# Generate predicted probability curve
plot_engine(model_logit)

4. Panel Data (Fixed/Random Effects)

Hausman test automatically selects between fixed and random effects.

model_panel <- paper_engine(wage ~ experience + education, 
                            data = panel_data, 
                            model = "panel",
                            entity_id = "worker_id",
                            time_id = "year",
                            method = "auto")  # Hausman test decides

plot_engine(model_panel)

5. Instrumental Variables (2SLS)

Detects weak instruments (Stock & Yogo, 2005) and tests overidentification (Sargan).

model_iv <- paper_engine(education ~ income, 
                         data = wage_data,
                         model = "iv",
                         instruments = ~ father_education + region)

# Diagnostics include first-stage F-stat
model_iv$messages
plot_engine(model_iv)

6. Difference-in-Differences

Tests parallel trends assumption and visualizes treatment effects.

model_did <- paper_engine(outcome ~ 1, 
                          data = policy_data,
                          model = "did",
                          treatment_var = "treated",
                          time_var = "period",
                          treatment_level = "Treated",
                          post_level = "Post")

# Plot shows parallel trends and treatment effect
plot_engine(model_did)

📊 Example Dataset

The package includes academic_salaries, a real dataset of 397 U.S. college professors:

data(academic_salaries)

# Explore salary determinants
salary_model <- paper_engine(salary ~ rank + discipline + years_since_phd + sex,
                             data = academic_salaries,
                             model = "ols",
                             robust = "auto")

🔬 Numerical Validation

All engines have been validated against standard R packages (lm, aov, glm, plm, ivreg) with numerical precision < 0.001. See validation.R for complete verification.

📖 Citation

To cite OLSengine in publications:

Soto-Pérez, M. (2025). OLSengine: Transparent linear and causal inference 
models for social sciences (v1.1.0). R package. 
https://github.com/msoto-perez/OLSengine

🤝 Contributing

Issues and pull requests are welcome at: https://github.com/msoto-perez/OLSengine/issues

📄 License

MIT License - see LICENSE file for details.