NEW FEATURES
newcustomer() prediction: Include the initial transaction in the predicted number of orders
NEW FEATURES
clvdata(data.end): Add parameter data.endto specify a data end beyond the last actual transaction
summary(): Always set zval andpval to NA for the main model parameters
hessian(): Add method to calculate hessian matrix for already fitted models
- Add 3 new vignettes covering: Advanced modelling techniques, model intuition, and the internal class system
BUG FIXES
- Fix CRAN notes: Replace
arma::is_finite() ->std::isfinite()
- Dyncov PNBD: Rename
predicted.CLV ->predicted.period.CLV
predict(): Rename{predicted, actual}.total.spending ->{predicted, actual}.period.spending
NEW FEATURES
newcustomer.spending(): Predict average spending per transaction for customers without order history
- Improved optimizer defaults (higher iteration count) for PNBD dyncov
NEW FEATURES
- Updated the apparel example data
- Prediction bootstrapping: Calculate confidence intervals using regular rather than “reversed-quantiles”
BUG FIXES
- Prediction bootstrapping: Re-fit model using exact original specification
- GGomNBD: Set limit in integration method to size of workspace
NEW FEATURES
- More memory efficient and faster creation of repeat transactions in
clv.data
- Use existing repeat transactions when calling
gg withremove.first.transaction = TRUE
- Simplify the formula interfaces
latentAttrition() andspending()
- 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 toclvdata
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
BUG FIXES
- 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
NEW FEATURES
- 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
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