Metabolomics data are inevitably subject to a component of unwanted
variation, due to factors such as batch effects, matrix effects, and confounding
biological variation. This package is a collection of functions designed to
implement, assess, and choose a suitable normalization method
for a given metabolomics study (De Livera et al (2015)
|Author||Alysha M De Livera, Gavriel Olshansky|
|Date of publication||2017-10-22 21:28:55 UTC|
|Maintainer||Alysha M De Livera <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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