`NEWS.md`

- Refactor the Gamma-Gamma (GG) model to predict mean spending per transaction into an independent model
- The prediction for transaction models can now be combined with separately fit spending models
- Write the unconditional expectation functions in Rcpp for faster plotting (Pareto/NBD and Beta-Geometric/NBD)
- Improved documentation and walkthrough

- Beta-Geometric/NBD (BG/NBD) model to predict repeat transactions without and with static covariates
- Gamma-Gompertz (GGompertz) model to predict repeat transactions without and with static covariates
- Predictions are now possible for all periods >= 0 whereas before a minimum of 2 periods was required

- Initial release of the CLVTools package

- Pareto/NBD model to predict repeat transactions without and with static or dynamic covariates
- Gamma-Gamma model to predict average spending
- Predicting CLV and future transactions per customer
- Data class to pre-process transaction data and to provide summary statistics
- Plot of expected repeat transactions as by the fitted model compared against actuals

Developed by Patrick Bachmann, Niels Kuebler, Markus Meierer, Jeffrey Naef, Elliot Oblander, Patrik Schilter.

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