Package: BTYDplus 1.2.0
BTYDplus: Probabilistic Models for Assessing and Predicting your Customer Base
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>].
Authors:
BTYDplus_1.2.0.tar.gz
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BTYDplus.pdf |BTYDplus.html✨
BTYDplus/json (API)
NEWS
# Install 'BTYDplus' in R: |
install.packages('BTYDplus', repos = c('https://mplatzer.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mplatzer/btydplus/issues
- groceryElog - Event log for customers of an online grocery store.
crmcustomer-behaviormarketing-sciencepredictive-analytics
Last updated 8 months agofrom:254e008e88. Checks:OK: 4 NOTE: 5. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win-x86_64 | NOTE | Nov 04 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 04 2024 |
R-4.4-win-x86_64 | NOTE | Nov 04 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 04 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 04 2024 |
R-4.3-win-x86_64 | OK | Nov 04 2024 |
R-4.3-mac-x86_64 | OK | Nov 04 2024 |
R-4.3-mac-aarch64 | OK | Nov 04 2024 |
Exports:abe.GenerateDataabe.mcmc.DrawParametersbgcnbd.cbs.LLbgcnbd.ConditionalExpectedTransactionsbgcnbd.EstimateParametersbgcnbd.Expectationbgcnbd.ExpectedCumulativeTransactionsbgcnbd.GenerateDatabgcnbd.LLbgcnbd.PAlivebgcnbd.PlotFrequencyInCalibrationbgcnbd.PlotFreqVsConditionalExpectedFrequencybgcnbd.PlotRecVsConditionalExpectedFrequencybgcnbd.PlotTrackingCumbgcnbd.PlotTrackingIncbgcnbd.pmfelog2cbselog2cumelog2incestimateRegularitymbgcnbd.cbs.LLmbgcnbd.ConditionalExpectedTransactionsmbgcnbd.EstimateParametersmbgcnbd.Expectationmbgcnbd.ExpectedCumulativeTransactionsmbgcnbd.GenerateDatambgcnbd.LLmbgcnbd.PAlivembgcnbd.PlotFrequencyInCalibrationmbgcnbd.PlotFreqVsConditionalExpectedFrequencymbgcnbd.PlotRecVsConditionalExpectedFrequencymbgcnbd.PlotTrackingCummbgcnbd.PlotTrackingIncmbgcnbd.pmfmbgnbd.EstimateParametersmcmc.DrawFutureTransactionsmcmc.Expectationmcmc.ExpectedCumulativeTransactionsmcmc.PActivemcmc.PAlivemcmc.PlotFrequencyInCalibrationmcmc.plotPActiveDiagnosticmcmc.PlotTrackingCummcmc.PlotTrackingIncmcmc.pmfmcmc.setBurninnbd.cbs.LLnbd.ConditionalExpectedTransactionsnbd.EstimateParametersnbd.GenerateDatanbd.LLpggg.GenerateDatapggg.mcmc.DrawParameterspggg.plotRegularityRateHeterogeneityplotTimingPatternspnbd.GenerateDatapnbd.mcmc.DrawParameters
Dependencies:bayesmBTYDclicodacontfracdata.tabledeSolvedplyrellipticfansigenericsgluehypergeolatticelifecyclemagrittrMASSMatrixmvtnormnloptrnumDerivoptimxpillarpkgconfigpracmaR6RcppRcppArmadillorlangtibbletidyselectutf8vctrswithr