Package {OLSengine}

Type:	Package
Title:	Transparent and Assisted Linear Modeling Engine
Version:	1.1.0
Description:	Unified estimation, diagnostics, and reporting for ordinary least squares (OLS) regression, ANOVA/t-tests, logistic regression, panel data (fixed/random effects with Hausman test), instrumental variables (2SLS with weak instrument diagnostics), and difference-in-differences. Designed for applied researchers in social sciences with integrated "Methodological Customs" that audit assumptions and provide literature references. All methods implemented in pure base R without external dependencies beyond stats and graphics packages.
License:	MIT + file LICENSE
Encoding:	UTF-8
Depends:	R (≥ 3.5.0)
Imports:	stats, graphics
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
URL:	https://github.com/msoto-perez/OLSengine
BugReports:	https://github.com/msoto-perez/OLSengine/issues
RoxygenNote:	7.3.3
LazyData:	true
NeedsCompilation:	no
Packaged:	2026-07-13 14:30:11 UTC; msoto
Author:	Manuel Soto-Pérez [aut, cre]
Maintainer:	Manuel Soto-Pérez <msoto@up.edu.mx>
Repository:	CRAN
Date/Publication:	2026-07-13 22:20:08 UTC

Academic Salaries Dataset for U.S. College Professors

Description

Real data on 9-month academic salaries for assistant professors, associate professors, and full professors at a U.S. college. This dataset is provided for educational purposes to demonstrate regression modeling, ANOVA, and logistic regression with paper_engine.

Usage

academic_salaries

Format

A data frame with 397 observations and 7 variables:

rank

Factor with 3 levels: "AsstProf" (Assistant Professor), "AssocProf" (Associate Professor), "Prof" (Full Professor). Represents academic rank.

discipline

Factor with 2 levels: "A" (theoretical departments, e.g., mathematics, physics) and "B" (applied departments, e.g., engineering, business). Represents academic discipline category.

years_since_phd

Numeric. Number of years since the faculty member earned their PhD.

years_service

Numeric. Number of years the faculty member has served at this institution.

sex

Factor with 2 levels: "Female" and "Male".

salary

Numeric. Nine-month academic salary in U.S. dollars (2008-09 academic year).

high_earner

Integer. Binary indicator (0 = No, 1 = Yes) marking faculty in the top 33% of salaries. Created for logistic regression demonstrations.

Details

This dataset enables demonstration of OLSengine's three core methods:

• OLS Regression: Modeling salary as a function of rank, discipline, experience, and sex to assess wage determinants and potential gender disparities.

• ANOVA: Comparing mean salaries across academic ranks or disciplines.

• Logistic Regression: Predicting the probability of being a high earner based on experience, rank, and discipline.

The data were collected in the 2008-09 academic year and reflect institutional salary structures at that time. Gender wage gap research in academia remains an active area of inquiry (Ginther & Kahn, 2021).

Source

This dataset is adapted from the Salaries dataset in the carData package (Fox & Weisberg, 2019), which was originally compiled for the textbook An R Companion to Applied Regression (Fox & Weisberg, 2011). The original data source is a U.S. college during the 2008-09 academic year.

Licensed under GPL (>= 2), consistent with the carData package license.

References

Fox, J., & Weisberg, S. (2011). An R Companion to Applied Regression (2nd ed.). Thousand Oaks, CA: Sage. https://www.john-fox.ca/Companion/

Fox, J., & Weisberg, S. (2019). carData: Companion to Applied Regression Data Sets. R package version 3.0-3. https://CRAN.R-project.org/package=carData

Ginther, D. K., & Kahn, S. (2021). Women in academic science: A changing landscape. Psychological Science in the Public Interest, 22(1), 3-65.

Examples

# Load the dataset
data(academic_salaries)

# Explore structure
str(academic_salaries)
summary(academic_salaries)

# OLS: Modeling salary determinants
ols_model <- paper_engine(
  salary ~ rank + discipline + years_since_phd + sex,
  data = academic_salaries,
  model = "ols",
  robust = "auto"
)
print(ols_model$tables$Table2_OLS_Estimation)
print(ols_model$messages)

# ANOVA: Salary differences across academic ranks
anova_model <- paper_engine(
  salary ~ rank,
  data = academic_salaries,
  model = "anova"
)
print(anova_model$tables$Descriptive_Means)

# Logit: Predicting high earner status
logit_model <- paper_engine(
  high_earner ~ years_since_phd + rank + discipline,
  data = academic_salaries,
  model = "logit"
)
print(logit_model$tables$Table2_Logit_Estimation)

# Visualization
plot_engine(ols_model)

Transparent and Assisted Linear Modeling Engine

Description

Estimates OLS regression, ANOVA/t-tests, binary logistic regression, panel data models, instrumental variables, or difference-in-differences using pure base R matrix algebra. Automatically audits statistical assumptions through an integrated methodological customs layer and returns publication-ready APA-formatted tables. Designed for applied researchers and early-career academics who need a single, transparent workflow from estimation to reporting.

Usage

paper_engine(
  formula,
  data,
  model = "ols",
  robust = FALSE,
  non_parametric = FALSE,
  paired = FALSE,
  entity_id = NULL,
  time_id = NULL,
  method = "auto",
  instruments = NULL,
  treatment_var = NULL,
  time_var = NULL,
  treatment_level = NULL,
  post_level = NULL,
  digits = 2
)

Arguments

Value

An object of class basic_model, which is a list containing:

tables

A list of formatted data frames with estimation results.

diagnostics

A list of raw diagnostic statistics (p-values, fit indices).

messages

A character vector of methodological guidance messages from the customs layer.

method

A character string indicating the engine used ("ols", "anova", "logit", "panel", "iv", or "did").

data

The cleaned data frame used for estimation (after listwise deletion).

Examples

# OLS example
set.seed(42)
df <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100))
result <- paper_engine(y ~ x1 + x2, data = df, model = "ols")
print(result$tables)
print(result$messages)

# ANOVA example
df2 <- data.frame(score = c(rnorm(30, 5), rnorm(30, 7)),
                  group = rep(c("A", "B"), each = 30))
result2 <- paper_engine(score ~ group, data = df2, model = "anova")
print(result2$tables)

# Logit example
df3 <- data.frame(y = rbinom(100, 1, 0.5), x = rnorm(100))
result3 <- paper_engine(y ~ x, data = df3, model = "logit")
print(result3$tables)

Generate Publication-Ready Plots for Basic Models

Description

Produces minimalist APA-style plots from a basic_model object returned by paper_engine. The plot type is selected automatically based on the estimation method: a forest plot of coefficients with 95 and a logistic probability curve for logistic regression.

Usage

plot_engine(model_object, y_label = NULL, x_label = NULL)

Arguments

Value

A base R plot rendered in the active graphics device. The function is called for its side effect (the plot) and returns NULL invisibly.

Examples

set.seed(42)
df <- data.frame(y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100))
result <- paper_engine(y ~ x1 + x2, data = df, model = "ols")
plot_engine(result, y_label = "Outcome", x_label = "Predictors")