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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 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
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.