  Mixture models are commonly used when data show signs of heterogeneity and,
often, it is important to estimate the distribution of the latent variable
responsible for that heterogeneity. This is a common problem for data taking
values in a Euclidean space, but the work on mixing distribution estimation
based on directional data taking values on the unit sphere is limited. In this
paper, we propose using the predictive recursion (PR) algorithm to solve for a
mixture on a sphere. One key feature of PR is its computational efficiency.
Moreover, compared to likelihood-based methods that only support finite mixing
distribution estimates, PR is able to estimate a smooth mixing density. PR's
asymptotic consistency in spherical mixture models is established, and
simulation results showcase its benefits compared to existing likelihood-based
methods. We also show two real-data examples to illustrate how PR can be used
for goodness-of-fit testing and clustering.
