OLSengine: Transparent and Assisted Linear Modeling in Base R 🚀

OLSengine is an open-source R package designed for applied researchers. It provides a robust, zero-dependency mathematical engine for fundamental parametric and non-parametric statistics (OLS, ANOVA, and Logit).

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

• 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) 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 (Cohen’s f2, Partial Eta-squared, Odds Ratios) and exact p-values.
• Minimalist Visualization: Generates publication-ready, grayscale plots (Forest Plots, Means with 95% CIs, and Logistic Probability Curves) without requiring ggplot2.

📦 Installation

You can install the development version of OLSengine directly from GitHub using:

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

🛠️ Usage Examples

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

1. OLS Regression (with Robust SE guidance)

Detects heteroskedasticity and multicollinearity. Users can explicitly request HC3 robust standard errors.

library(OLSengine)

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

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

# View APA-ready table
model_robust$tables$Table2_OLS_Estimation

# Generate Forest Plot
plot_engine(model_robust)

2. Experimental Differences (4-Way ANOVA Engine)

Intelligently handles independent or paired designs, supporting both parametric and non-parametric equivalents (One-Way ANOVA, Kruskal-Wallis, Paired t-test, Wilcoxon).

# Let the "Customs" automatically switch to Non-Parametric if Normality fails
model_exp <- paper_engine(y ~ group, data = experiment_data, model = "anova", non_parametric = "auto")

# Generate Means Plot with 95% CI error bars
plot_engine(model_exp)

3. Binary Logistic Regression (Logit)

Reports Odds Ratios, McFadden’s Pseudo R-Squared, and Classification Accuracy.

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

# Generate predicted probability curve
plot_engine(model_logit)

📖 Citation

To cite OLSengine in publications, please use:

Soto-Pérez, M. (2026).

OLSengine: A transparent and assisted linear modelling engine in base R (v1.0.0).

Zenodo. https://doi.org/10.5281/zenodo.19375852