This paper reviews recent developments in the area of computer-assiste
d analysis of mixture distributions (C.A.MAN). Given a biometric situa
tion of interest in which, under homogeneity assumptions, a certain pa
rametric density occurs, such as the Poisson, the binomial, the geomet
ric, the normal, and so forth, then it is argued that this situation c
an easily be enlarged to allow a variation of the scalar parameter in
the population. This situation is called unobserved heterogeneity. Thi
s naturally leads to a specific form of nonparametric mixture distribu
tion that can then be assumed to be the standard model in the biometri
c application of interest (since it also incorporates the homogeneous
situations as a special case). Besides developments in theory and algo
rithms, the work focuses on developments in biometric applications suc
h as meta-analysis, fertility studies, estimation of prevalence under
clustering, and estimation of the distribution function of survival ti
me under interval censoring. The approach is nonparametric for the mix
ing distribution, including leaving the number of components (subpopul
ations) of the mixing distribution unknown.