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snazzieR (version 0.1.2)

Chic and Sleek Functions for Beautiful Statisticians

Description

Because your linear models deserve better than console output. A sleek color palette and kable styling to make your regression results look sharper than they are. Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms, along with a unified interface for model fitting and fabulous LaTeX and console output formatting. See the package website at .

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Version

Install

install.packages('snazzieR')

Monthly Downloads

192

Version

0.1.2

License

MIT + file LICENSE

Maintainer

Aidan J. Wagner

Last Published

December 16th, 2025

Functions in snazzieR (0.1.2)

ridge.summary

Format Ridge Model Output as LaTeX Tables
ridge.regression

Ridge Regression with Automatic Lambda Selection
kfold_cross_validation

Perform K-Fold Cross Validation
model.equation

Generate a Model Equation from a Linear Model
format.pls

Format PLS Model Output as LaTeX or Console Tables
ANOVA.summary.table

Generate a Summary Table for ANOVA Results
cv.mse

Compute Cross-Validated Mean Squared Error
SVD.pls

Partial Least Squares Regression via SVD (Internal)
create_kfold_splits

Create K-Fold Cross Validation Splits
color.ref

Display a Color Reference Palette
eigen.summary

Summarize Eigenvalues and Eigenvectors of a Covariance Matrix
pls.summary

Format PLS Model Output as LaTeX Tables
snazzieR.theme

A Custom ggplot2 Theme for Publication-Ready Plots
pls.regression

Partial Least Squares (PLS) Regression Interface
fit.ridge

Fit Ridge Regression with Closed-Form Solution
colors

SnazzieR Color Palette
NIPALS.pls

Partial Least Squares Regression via NIPALS (Internal)
model.summary.table

Generate a Summary Table for a Linear Model
optimize.cv.lambda

Optimize Lambda Using Cross-Validation
predict.ridge.model

Predict Method for Ridge Model Objects
print.ridge.model

Print Method for Ridge Model Objects