Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

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 LDLt
    (Cholesky) decomposition.
    McLachlan, G. and Peel, D. (2000, ISBN:9780471006268) "Finite Mixture Models".
    Celeux, G. and Govaert, G. (1995) <doi:10.1016/0031-3203(94)00125-6> "Gaussian parsimonious clustering models".
    Marron, S. and Wand, M. (1992) <doi:10.1214/aos/1176348653> "Exact Mean Integrated Squared Error".

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.