Probabilistic Models for Assessing and Predicting your Customer
Base
person("Michael", "Platzer", email = "michael.platzer@gmail.com", role = c("aut", "cre"))
Provides advanced statistical methods to describe and predict customers'
purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
package complements the BTYD package by providing several additional buy-till-you-die models, that
have been published in the marketing literature, but whose implementation are complex and non-trivial.
These models are: NBD, MBG/NBD, BG/CNBD-k, MBG/CNBD-k, Pareto/NBD (HB), Pareto/NBD (Abe) and Pareto/GGG.
Rcpp, BTYD (>= 2.3), coda, data.table, mvtnorm, bayesm, stats,
graphics
testthat, gsl, covr, knitr, rmarkdown, lintr (>= 1.0.0)

Provides advanced statistical methods to describe and predict customers'
purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
package complements the BTYD package by providing several additional buy-till-you-die models, that
have been published in the marketing literature, but whose implementation are complex and non-trivial.
These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007)
<doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020)
<doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009) <doi:10.1287/mksc.1090.0502>]
and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].
testthat, covr, knitr, rmarkdown, gsl, lintr (>= 1.0.0)

Bradley-Terry Model with Exponential Time Decayed Log-Likelihood
and Adaptive Lasso
c(person("Yunpeng", "Zhou", email = "michael.zhou.hku@gmail.com", role = c("aut","cre")),
person("Jinfeng", "Xu", role = "aut"))
We apply Bradley-Terry Model to estimate teams' ability in paired comparison data. Exponential Decayed Log-likelihood function is applied for dynamic approximation of current rankings and Lasso penalty is applied for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
Yunpeng Zhou [aut, cre],
Jinfeng Xu [aut]