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Modish

0.1.0

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)

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