{BTYD}R Documentation

Pareto/NBD Log-Likelihood


Calculates the log-likelihood of the Pareto/NBD model.

Usage,, hardie = TRUE)



Pareto/NBD parameters - a vector with r, alpha, s, and beta, in that order. r and alpha are unobserved parameters for the NBD transaction process. s and beta are unobserved parameters for the Pareto (exponential gamma) dropout process.

calibration period CBS (customer by sufficient statistic). It must contain columns for frequency ("x"), recency ("t.x"), and total time observed (""). Note that recency must be the time between the start of the calibration period and the customer's last transaction, not the time between the customer's last transaction and the end of the calibration period. If your data is compressed (see, a fourth column labelled "custs" (number of customers with a specific combination of recency, frequency and length of calibration period) will make this function faster.


if TRUE, use h2f1 instead of hypergeo.


The log-likelihood of the provided data.


Fader, Peter S., and Bruce G.S. Hardie. "A Note on Deriving the Pareto/NBD Model and Related Expressions." November. 2005. Web.

See Also




data(cdnowSummary) <- cdnowSummary$cbs
# already has column names required by method

# random assignment of parameters
params <- c(0.5, 8, 0.7, 10)
# returns the log-likelihood of the given parameters (params,, TRUE)

# compare the speed and results to the following: <- (params,, TRUE)

[Package BTYD version 2.4.3 Index]