newcustomer() prediction: Include the initial
transaction in the predicted number of ordersclvdata(data.end): Add parameter data.end
to specify a data end beyond the last actual transactionsummary(): Always set zval and
pval to NA for the main model parametershessian(): Add method to calculate hessian matrix for
already fitted modelsarma::is_finite() ->
std::isfinite()predicted.CLV ->
predicted.period.CLVpredict(): Rename
{predicted, actual}.total.spending ->
{predicted, actual}.period.spendingnewcustomer.spending(): Predict average spending per
transaction for customers without order historyclv.datagg with
remove.first.transaction = TRUElatentAttrition() and
spending()predicted.total.spending to predictionsdata.table::IDate as data inputs to
clvdatasummary.clv.data:Much faster by improving the
calculation of the mean inter-purchase timeSetDynamicCovariates: Verify there is no covariate data
for nonexistent customerslatentAttrition() and spending())plot.clv.data(which='timings'))plot(other.models=list(), label=c()))
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