Growth trajectories play a central role in life course epidemiology,
often providing fundamental indicators of prenatal or childhood
development, as well as an array of potential determinants of
adult health outcomes.  Statistical methods for the analysis of growth
trajectories have been widely studied, but many challenging
problems remain.  Repeated measurements of length, weight and head
circumference, for example, may be available on most subjects in a
study, but usually only sparse temporal sampling of such variables is
feasible.  It can thus be challenging to gain a detailed understanding 
of growth velocity patterns, and smoothing techniques are inevitably needed.
  Moreover, the problem is exacerbated by the presence of large fluctuations in growth
velocity during early infancy, and high variability between subjects.
 Existing approaches, however, can be inflexible due to a reliance on
parametric models, and require computationally intensive methods that are unsuitable
for exploratory analyses. This talk introduces a nonparametric Bayesian inversion 
approach to such problems, along with an R package that implements the proposed method.

The talk is based on joint work with Sara Lopez-Pintado.