The R Package trafo for Transforming Linear Regression Models

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.

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

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

trafo, car, rcompanio, bestNormalize, caret, Johnson, jtrans, MASS, AID, Ecdat

CRAN Task Views implied by cited packages

Econometrics, Multivariate, SocialSciences, TeachingStatistics, Distributions, Environmetrics, Finance, HighPerformanceComputing, MachineLearning, NumericalMathematics, Psychometrics, Robust, TimeSeries


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For attribution, please cite this work as

Medina, et al., "The R Journal: The R Package trafo for Transforming Linear Regression Models", The R Journal, 2020

BibTeX citation

  author = {Medina, Lily and Kreutzmann, Ann-Kristin and Rojas-Perilla, Natalia and Castro, Piedad},
  title = {The R Journal: The R Package trafo for Transforming Linear Regression Models},
  journal = {The R Journal},
  year = {2020},
  note = {},
  doi = {10.32614/RJ-2019-054},
  volume = {11},
  issue = {2},
  issn = {2073-4859},
  pages = {99-123}