The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Finding Optimal Normalizing Transformations via bestNormalize
Open accessAbstract
The bestNormalize R package was designed to help users find a transformation that can effectively normalize a vector regardless of its actual distribution. Each of the many normalization techniques that have been developed has its own strengths and weaknesses, and deciding which to use until data are fully observed is difficult or impossible. This package facilitates choosing between a range of possible transformations and will automatically return the best one, i.e., the one that makes data look the most normal. To evaluate and compare the normalization efficacy across a suite of possible transformations, we developed a statistic based on a goodness of fit test divided by its degrees of freedom. Transformations can be seamlessly trained and applied to newly observed data and can be implemented in conjunction with caret and recipes for data preprocessing in machine learning workflows. Custom transformations and normalization statistics are supported.
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An Analysis of Transformations
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ggplot2: Elegant Graphics for Data Analysis
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“Another increasingly popular application of normalization occurs in applied regression settings with highly skewed distributions of the covariates (Kuhn and Johnson, 2013).”
BackgroundModern Applied Statistics With S
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caret: Classification and Regression Training
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Cited by
Williamson, D., Schwalbe, E. C., Hicks, D., Aldinger, K. A., Lindsey, J. C., Crosier, S., Richardson, S., Goddard, J., Hill, R. M., Castle, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111162 · Cited by 87
Metadata
- Authors
- Peterson, R.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2021
- Volume
- 13
- Issue
- 1
- Pages
- 310
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W3166154243
- Cited by
- 478
- References
- 20
- FWCI
- 22.85
- Type
- journal article
- Language
- en
- Open access
- diamond
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