It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets.
             Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.

Cleans and Normalizes FLUOstar DBF and DAT Files obtained from liposome flux assays. Users should verify extended usage of the package on files from other assay types.

Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes.
    Fitting models to data using 'MLE' (maximum likelihood estimation) for
    multivariate normal mixtures via smart parametrization using the 'LDL'
    (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268),
    Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.

Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.

A toolbox for continuous norming of psychological and educational tests, supporting regression-based norming where norms can vary as a continuous function of age or another norm predictor. Norms are estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), enabling flexible modelling of the full score distribution in a normative sample. The package supports applications in psychometrics and psychological testing, and includes functions for model selection, reliability estimation, norm calculation, including confidence intervals, and sample size planning. For more details, see Timmerman et al. (2021) <doi:10.1037/met0000348>.