Lifetimes

- a version bump for conda packaging

- some convergence improvements

- bump the Pandas requirements to >= 0.24.0. This should have been done in 0.11.0
- suppress some warnings from autograd.

- Move most models (all but Pareto) to autograd for automatic differentiation of their likelihood. This results in faster (at least 3x) and more successful convergence, plus allows for some really exciting extensions (coming soon).
- `GammaGammaFitter`, `BetaGeoFitter`, `ModifiedBetaGeoFitter` and `BetaGeoBetaBinomFitter` have three new attributes: `confidence_interval_`, `variance_matrix_` and `standard_errors_`
- `params_` on fitted models is not longer an OrderedDict, but a Pandas Series
- `GammaGammaFitter` can accept a `weights` argument now.
- `customer_lifelime_value` in `GammaGamma` now accepts a frequency argument.
- fixed a bug that was causing `ParetoNBDFitter` to generate data incorrectly.

- performance improvements to `generate_data.py` for large datasets 195
- performance improvements to `summary_data_from_transaction_data`, thanks MichaelSchreier
- Previously, `GammaGammaFitter` would have an infinite mean when its `q` parameter was less than 1. This was possible for some datasets. In 0.10.1, a new argument is added to `GammaGammaFitter` to constrain that `q` is greater than 1. This can be done with `q_constraint=True` in the call to `GammaGammaFitter.fit`. See issue 146. Thanks vruvora
- Stop support of scipy < 1.0.
- Stop support of < Python 3.5.

- `BetaGeoBetaBinomFitter.fit` has replaced `n_custs` with the more appropriately named `weights` (to align with other statisical libraries). By default and if unspecified, `weights` is equal to an array of 1s.
- The `conditional_` methods on `BetaGeoBetaBinomFitter` have been updated to handle exogenously provided recency, frequency and periods.
- Performance improvements in `BetaGeoBetaBinomFitter`. `fit` takes about 50% less time than previously.
- `BetaGeoFitter`, `ParetoNBDFitter`, and `ModifiedBetaGeoFitter` both have a new `weights` argument in their `fit`. This can be used to reduce the size of the data (collapsing subjects with the same recency, frequency, T).