Transparent and Assisted Linear Modeling Engine.
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