CLVTools 0.9.0

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

CLVTools 0.8.1

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

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