Package 'OLSengine'

Transparent and Assisted Linear Modeling Engine

Description

Estimates OLS regression, ANOVA/t-tests, or binary logistic regression models 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,
  digits = 2
)

Arguments

formula	A formula object specifying the model (e.g., y ~ x1 + x2).
data	A data frame containing all variables referenced in formula.
model	A character string indicating the estimation engine. One of "ols" (default), "anova", or "logit".
robust	Logical or "auto". Controls heteroskedasticity-robust standard errors (HC3) for OLS models. If TRUE, HC3 SEs are always applied. If "auto", they are applied only when the Breusch-Pagan test detects heteroskedasticity (p < .05). Default is FALSE.
non_parametric	Logical or "auto". Controls non-parametric fallback for ANOVA/t-test models. If TRUE, Kruskal-Wallis or Wilcoxon tests are used. If "auto", transition occurs when Shapiro-Wilk detects non-normality (p < .05). Default is FALSE.
paired	Logical. If TRUE, assumes paired/dependent samples for ANOVA/t-test models (pre-post designs). Default is FALSE.
digits	Integer. Number of decimal places in output tables. Default is 2.

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", or "logit").

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% CI for OLS, a group means plot with 95% CI error bars for ANOVA, and a logistic probability curve for logistic regression.

Usage

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

Arguments

model_object	An object of class basic_model generated by paper_engine.
y_label	A character string for the Y-axis label. If NULL (default), a label is generated automatically from the model type.
x_label	A character string for the X-axis label. If NULL (default), a label is generated automatically from the model type.

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")

---