MIX-EXTENSIONS: Possible extensions to the mixture modeling software. The mixture modeling software presently provides the core inferential facilities for simple models, but does not do many things that one would often need to use these models in practice. Here are some extensions that would be feasible to add, and which may be added one day (though I make no guarantees). They are presented in roughly increasing order of difficulty of implementation. 1) The concentration parameter of the Dirichlet prior for component mixing proportions could be a variable hyperparameter, rather than being constant as as present. 2) A program could be written for giving the predictive density at a given point (ie, for a given set of target values). 3) Models for problems in which some attributes are binary and some are real could be supported. Attributes taking on values from some finite set with more than two elements could also be supported. 4) Models in which the distribution of the target variables depends on a set of input variables (eg, via a linear regression model) could be supported. 5) Support for missing targets in the training cases (assumed to be missing at random) could be provided. A program could also be written that fills in missing targets in test cases, based on the targets that are not missing. 6) The shape parameters in the various priors could be made variable hyperparameters. This may be more important for mixture models than for neural network and Gaussian process models, because of the effects these shape parameters have on how many mixture components get used (due to "Occam" effects).