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Implementation of statistical models to analyze time lagged conversions

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Steps to reproduce: - clone the repo - `python -m venv venv` and `source venv/bin/activate` - `pip install convoys==0.2.1` - `python examples/dob_violations.py` Stacktrace: File "examples/dob_violations.py", line 50, in run() File...

The commentary of the chart with both Weibull and Kaplan-Meier curves mistakenly reverses the solid/dashed pattern.

I am running into an numpy type error (see below) whenever I set value for the parameters `mcmc==True` or `ci==0.95` in any convoys model. This is consistent across data sources...

This is to be merged in maybe 3-6 months, to give people a chance to change how they use convoys.

Calling fit on `GeneralizedGamma` within a Jupyter Labs notebook results in a lot of whitespace in the output. I think this is because `sys.stdout.flush()` does not play well Labs. ![Master](https://user-images.githubusercontent.com/13262878/56432799-ccafbc00-629c-11e9-9e3e-e2737a9ff4d2.gif)

http://data.princeton.edu/pop509/ParametricSurvival.pdf I don't see an obvious pdf in that documentation though. Perhaps I need to re-read it though.

`numpy.int` was deprecated in `1.2.0` [[ref](https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations)] This type is being used in the [Gamma model ](https://github.com/better/convoys/blob/99e832d2170ba9670e5e16bb3f632ac9055291f3/convoys/multi.py#L25) Happy to raise a PR to change to `numpy.int_`

Add `colormap` argument to `plot_cohorts` so that line plots for each group can have an ordinal colour. For example: ![example](https://github.com/better/convoys/assets/90821425/691cb3b6-6203-4b7d-9e3b-01ba9529d318)