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CLVTools 0.11.2

NEW FEATURES

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

CLVTools 0.11.1

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

CLVTools 0.11.0

NEW FEATURES

  • 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

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

CLVTools 0.10.0

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

CLVTools 0.9.0

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

CLVTools 0.8.1

BUG FIXES

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

CLVTools 0.8.0

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

CLVTools 0.7.0

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

CLVTools 0.6.0

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

CLVTools 0.5.0

  • 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