Stabilized-ica is now compatible with scikit-learn API, meaning that you can use the base class as a sklearn transformer and include it in complex ML pipelines (see [this tutorial](https://github.com/ncaptier/stabilized-ica/blob/master/examples/MNIST_classification.ipynb) for an illustration).
sica.annotate and sica.singlecell modules have been removed from stabilized-ica and integrated into a complementary python toolbox called [sica-omics](https://github.com/ncaptier/sica-omics) . stabilized-ica no longer contains dependencies specific to omics data analysis.
**Fixed bugs:**
- svd_solver default value (parameter of sica._whitening.whitening) was changed from full (i.e full svd decomposition) to auto (i.e selection of most efficient solver for the size of the given dataset). This significantly speeds up the computation for large datasets.
**New features:**
- sica.base.MSTD has new fun and algorithm parameters so that the user can specify the ICA algorithm and the non-linearity function to use (for the previous version only algorithm = fastica_par and fun = 'logcosh' were available).