- Updated the apparel example data
- Prediction bootstrapping: Calculate confidence intervals using regular rather than “reversed-quantiles”

- Prediction bootstrapping: Re-fit model using exact original specification
- GGomNBD: Set limit in integration method to size of workspace

- More memory efficient and faster creation of repeat transactions in
`clv.data`

- Use existing repeat transactions when calling
`gg`

with`remove.first.transaction = TRUE`

- Simplify the formula interfaces
`latentAttrition()`

and`spending()`

- Add
`predicted.total.spending`

to predictions - Harmonize parameter names used in various S3 methods
- Bootstrapping: Add facilities to estimate parameter uncertainty for all models
- Ability to predict future transactions of customers with no existing transaction history
- New start parameters for all latent attrition models
- Pareto/NBD dyncov: Improved numeric stability of PAlive
- GGomNBD: Implement erratum by Jost Adler to predict CET correctly
- GGomNBD: Improve numerical stability and runtime of LL integral
- GGomNBD: Implement PMF as derived by Jost Adler
- lrtest(): Likelihood ratio testing for latent attrition models
- Accept
`data.table::IDate`

as data inputs to`clvdata`

`summary.clv.data`

:Much faster by improving the calculation of the mean inter-purchase time- Reduced fitting times for all models by using a compressed CBS as input to the LL sum
- Faster hessian calculation if a model was using correlation

- Estimating the Pareto/NBD dyncov with correlation was not possible
- GGomNBD: Free workspace after it is not used anymore to avoid memory-leak
`SetDynamicCovariates`

: Verify there is no covariate data for nonexistent customers

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

and`spending()`

) - New method to plot customer’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

- 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

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

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

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

- 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

- 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

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