Methodology for normalization with max value division

Hi! I am creating this issue to touch upon the way the tidyCovariateData function is normalizing the data when using Normalize=T.

From what I can see in the code, the data are normalized by dividing with the maximum value of each covariate:

https://github.com/OHDSI/FeatureExtraction/blob/main/R/Normalization.R (line 162)

if (normalize) { ParallelLogger::logInfo("Normalizing covariates") newCovariates <- newCovariates %>% inner_join(covariateData$maxValuePerCovariateId, by = "covariateId") %>% mutate(covariateValue = .data$covariateValue / .data$maxValue) %>% select(-.data$maxValue) metaData$normFactors <- covariateData$maxValuePerCovariateId %>% collect() } newCovariateData$covariates <- newCovariates }

I am interested to discuss the choice of this normalization method, since it is not really recommended in literature. Have you seen that it has some specific advanatages for machine learning models? From what I am aware of, most of the time methods like min-max or z-score are suggested. Is that maybe a feature for a potential future update or would you recommend some way to provide a custom normalization function on the current version of the package?

Thank you in advance for your consideration!

Start by reading R/Normalization.R around line 162 and the issue discussion about dividing each covariate by its maximum value. Determine whether the project wants a different normalization method, configurable normalization, or only a documented rationale; the work is done when maintainers agree on a specific change and its expected behavior.