This is the stable, essentially complete version of SEPIA. We anticipate only minor bugfixes or small feature additions in the future. Since v1.0.0, we have added: To set up a model, use SepiaModel(data) (as opposed to setup_model() function from first release) Prediction code finalized, now takes x and t in native (untransformed) space and handles transformations internally Added Kronecker-separable design awareness and capability Added ability to pass theta constraint functions to SepiaModel Added ability to use categorical variables in x and t Added more thorough testing Fixed some unexpected behavior in model.get_samples() Added get_samples() methods for SepiaHierarchicalThetaModels/SepiaSharedThetaModels Added to examples (in particular, added shared/hierarchical model example and parallel chains example) Simplified install directions and added dependencies in setup.py Sensitivity analysis finished and tested against Matlab GPMSA version Added to documentation