I would like to introduce a new package: cNORM. It aims at solving
problems with percentile estimation / norm score generation in
biometrics and psychometrics, f. e. BMI growth curves, IQ tests ...
Conventional methods for producing standard scores in psychometrics or
biometrics are often plagued with "jumps" or "gaps" (i.e.,
discontinuities) in norm tables and low confidence for assessing extreme
scores. The continuous norming method introduced by A. Lenhard et al.
(2016), <doi:10.1177/1073191116656437>, generates continuous test norm
scores on the basis of the raw data from standardization samples,
without requiring assumptions about the distribution of the raw data:
Norm scores are directly established from raw data by modeling the
latter ones as a function of both percentile scores and an explanatory
variable (e.g., age). The method minimizes bias arising from sampling
and measurement error, while handling marked deviations from normality,
addressing bottom or ceiling effects and capturing almost all of the
variance in the original norm data sample.