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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.
Health stats visible at Monitor.