  In this note, we explore a simple approach to composition estimation, using
penalized likelihood density estimation on a nominal discrete domain. Practical
issues such as smoothing parameter selection and the use of prior information
are investigated in simulations, and a theoretical analysis is attempted. The
method has been implemented in a pair of R functions for use by practitioners.
