The WORM PLOT is a method for fitting references curves by means of the LMS model. The method was published in S. van Buuren and A.M. Fredriks (2001). Worm plot: A simple diagnostic device for modeling growth reference curves. Statistics in Medicine, 20, 12591277. pdf
Abstract of the paper
The worm plot visualizes differences between two distributions, conditional on the values of a covariate. Though the worm plot is a general diagnostic tool for the analysis of residuals, this paper focuses on an application in constructing growth reference curves, where the covariate of interest is age. The LMS model of Cole and Green is used to construct reference curves in the Fourth Dutch Growth Study 1997. If the model fits, the measurements in the reference sample follow a standard normal distribution on all ages after a suitably chosen BoxCox transformation. The coefficients of this transformation are modeled as smooth agedependent parameter curves for the median, variation and skewness, respectively. The major modeling task is to choose the appropriate amount of smoothness of each parameter curve. The worm plot assesses the ageconditional normality of the transformed data under a variety of LMS models. The fit of each parameter curve is closely related to particular features in the worm plot, namely its offset, slope and curvature. Application of the worm plot to the Dutch growth data resulted in satisfactory reference curves for a variety of anthropometric measures. It was found that the LMS method generally models the ageconditional mean and skewness better than the agerelated deviation and kurtosis.
An S Plus Program for drawing the Worm plot

wp.ssc (S Plus code) 

example data (S Plus sdd dataset from Fourth Dutch Growth Study) 

Zscore data after fitting model 0/10/6r on example data (S Plus sdd dataset) 

Figure 2 from paper (nonfitting model 0/5/1r) 

Figure 4 from paper (nonfitting model 0/10/1r) 

Figure 5 from paper (fitting model 0/10/6r) 

Figure 6 from paper (overfitting model 4/10/6r) 
Worm plot in quantile regression
