The R Journal: article published in 2019, volume 11:2

The R Package trafo for Transforming Linear Regression Models
Lily Medina, Ann-Kristin Kreutzmann, Natalia Rojas-Perilla and Piedad Castro , The R Journal (2019) 11:2, pages 99-123.

Abstract 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.

Received: 2018-12-01; online 2020-01-06, supplementary material, (1000 B)
CRAN packages: trafo, car, rcompanio, bestNormalize, caret, Johnson, jtrans, MASS, AID, Ecdat
CRAN Task Views implied by cited CRAN packages: Econometrics, Multivariate, SocialSciences, TeachingStatistics, Distributions, Environmetrics, Finance, HighPerformanceComputing, MachineLearning, NumericalMathematics, Psychometrics, Robust, TimeSeries

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-054,
  author = {Lily Medina and Ann-Kristin Kreutzmann and Natalia Rojas-
          Perilla and Piedad Castro},
  title = {{The R Package trafo for Transforming Linear Regression
          Models}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-054},
  url = {https://doi.org/10.32614/RJ-2019-054},
  pages = {99--123},
  volume = {11},
  number = {2}
}