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This is the first release of `modish`, a modality estimator. To estimate modalities, there are three steps: 1. Initialize a `ModalityEstimator` 2. Calculate log2 Bayes factors for each splicing event 3. Assign modalities based on the largest Bayes Factor score (and log2 bayes factor cutoff, default 3) estimator = modish.ModalityEstimator() log2_bayes_factors = estimator.fit_transform(psi_filtered) modality_assignments = estimator.assign_modalities(log2_bayes_factors) To see the parameterized family created from the estimator, do import seaborn as sns estimator = modish.ModalityEstimator() `n` is the number of random variables to generate. Larger will result in smoother plots. fig = estimator.violinplot(n=10000) fig.tight_layout() for ax in fig.axes: ax.set(yticks=[0, 0.5, 1]) sns.despine() ![image](https://cloud.githubusercontent.com/assets/806256/8658993/61329a24-295c-11e5-84d9-838cbaafa594.png)