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Showing content from http://cran.rstudio.com/web/packages/uwot/../Rcpp/../binsegRcpp/../Rcpp/../CLVTools/news/news.html below:

NEWS

• newcustomer.spending(): Predict average spending per transaction for customers without order history
• Improved optimizer defaults (higher iteration count) for PNBD dyncov

• 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

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