The R package trafo for transforming linear regression models

Lily Medina, Ann Kristin Kreutzmann, Natalia Rojas-Perilla, Piedad Castro

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Researchers and data-analysts often use the linear regression model for descriptive, predictive, and inferential purposes. This model relies on a set of assumptions that, when not satisfied, yields biased results and noisy estimates. A common problem that can be solved in many ways-use of less restrictive methods (e.g. generalized linear regression models or non-parametric methods), variance corrections or transformations of the response variable just to name a few. We focus on the latter option as it allows to keep using the simple and well-known linear regression model. The list of transformations proposed in the literature is long and varies according to the problem they aim to solve. Such diversity can leave analysts lost and confused. We provide a framework implemented as an R-package, trafo, to help select suitable transformations depending on the user requirements and data being analyzed. The package trafo contains a collection of selected transformations and estimation methods that complement and increase the breadth of methods that exist in R.

Original languageEnglish
Pages (from-to)99-123
Number of pages25
JournalR Journal
Issue number2
Publication statusPublished - Dec 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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