The {MASS} R Package

The MASS package in R provides support functions and datasets for Venables and Ripley’s book, “Modern Applied Statistics with S” (4th edition, 2002) ¹. It’s one of the most popular R packages for performing modern applied statistics. You’ll find a wide variety of functions for tasks like linear and generalized linear models, density estimation, and more ². If you’re working with statistical models, MASS is a powerful tool to have in your toolbox! 😊

Source: Conversation with Copilot, 30/07/2024 (1) MASS package - RDocumentation. https://www.rdocumentation.org/packages/MASS/versions/7.3-61. (2) The Ultimate Guide to the MASS Package in R. https://rbasics.org/packages/mass-package-in-r/. (3) CRAN: Package MASS - The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/MASS/index.html.

Certainly! The MASS package in R provides a variety of functions for statistical modeling and data analysis. Here are some notable ones:

  1. lm: Fits linear regression models.
  2. glm: Fits generalized linear models.
  3. stepAIC: Performs stepwise model selection using AIC.
  4. lda: Performs linear discriminant analysis.
  5. qda: Performs quadratic discriminant analysis.
  6. polr: Fits ordinal logistic regression models.
  7. kde2d: Computes a 2D kernel density estimate.

These functions cover a range of statistical techniques, making MASS a versatile package for data scientists and statisticians! 😊📊

Certainly! In addition to the MASS package, there are several other R packages that provide similar functions for statistical modeling and analysis. Here are a few notable ones:

  1. stats: This is a core R package that includes functions for basic statistical operations, linear models, and hypothesis testing.
  2. glmnet: Specifically designed for fitting regularized generalized linear models (Lasso and Ridge regression).
  3. caret: A versatile package for training and evaluating machine learning models, including regression and classification.
  4. nnet: For fitting neural networks and multilayer perceptrons.
  5. survival: Focused on survival analysis and Cox proportional hazards models.

Feel free to explore these packages based on your specific needs! 😊📊