### NEW FEATURES

- We add an interface to specify models using a formula notation
(
`latentAttrition()`

and `spending()`

)
- New method to plot customerâ€™s transaction timings
(
`plot.clv.data(which='timings')`

)
- Draw diagnostic plots of multiple models in single plot
(
`plot(other.models=list(), label=c())`

)
- MUCH faster fitting for the Pareto/NBD with time-varying covariates
because we implemented the LL in Rcpp

### NEW FEATURES

- Three new diagnostic plots for transaction data to analyse
frequency, spending and interpurchase time
- New diagnostic plot for fitted transaction models (PMF plot)
- New function to calculate the probability mass function of selected
models
- Calculate summary statistics only for the transaction data of
selected customers
- Canonical transformation from data.frame/data.table to transaction
data object and vice-versa
- Canonical subset for the data stored in the transaction data
object
- Pareto/NBD DERT: Improved numerical stability

### BUG FIXES

- Fix importing issue after package lubridate does no longer use
Rcpp

### NEW FEATURES

- Partially refactor the LL of the extended Pareto/NBD in Rcpp with
code kindly donated by Elliot Shin Oblander
- Improved documentation

### BUG FIXES

- Optimization methods nlm and nlminb can now be used. Thanks to
Elliot Shin Oblander for reporting

### NEW FEATURES

- 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

### BUG FIXES

- Pareto/NBD log-likelihood: For the case Tcal = t.x and for the case
alpha == beta
- Static or dynamic covariates with syntactically invalid names
(spaces, start with numbers, etc) could not be fit

### NEW FEATURES

- 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

### NEW FEATURES

- 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 preprocess transaction data and to provide summary
statistics
- Plot of expected repeat transactions as by the fitted model compared
against actuals