All functions

CLVTools CLVTools-package

Customer Lifetime Value Tools

SetDynamicCovariates()

Add Dynamic Covariates to a CLV data object

SetStaticCovariates()

Add Static Covariates to a CLV data object

apparelDynCov

Time-varying Covariates for the Apparel Retailer Dataset

apparelStaticCov

Time-invariant Covariates for the Apparel Retailer Dataset

apparelTrans

Apparel Retailer Dataset

as.clv.data()

Coerce to clv.data object

as.data.frame(<clv.data>)

Coerce to a Data Frame

as.data.table(<clv.data>)

Coerce to a Data Table

bgbb(<clv.data>) bgbb(<clv.data.static.covariates>) bgbb(<clv.data.dynamic.covariates>)

BG/BB models - Work In Progress

bgnbd(<clv.data>) bgnbd(<clv.data.static.covariates>)

BG/NBD models

bgnbd_nocov_CET() bgnbd_staticcov_CET()

BG/NBD: Conditional Expected Transactions

bgnbd_nocov_LL_ind() bgnbd_nocov_LL_sum() bgnbd_staticcov_LL_ind() bgnbd_staticcov_LL_sum()

BG/NBD: Log-Likelihood functions

bgnbd_nocov_PAlive() bgnbd_staticcov_PAlive()

BG/NBD: Probability of Being Alive

bgnbd_nocov_expectation() bgnbd_staticcov_expectation()

BG/NBD: Unconditional Expectation

bgnbd_nocov_PMF() bgnbd_staticcov_PMF()

BG/NBD: Probability Mass Function (PMF)

cdnow

CDNOW dataset

clv.bootstrapped.apply()

Bootstrapping: Fit a model again on sampled data and apply method

clvdata()

Create an object for transactional data required to estimate CLV

fitted(<clv.fitted>)

Extract Unconditional Expectation

gg(<clv.data>)

Gamma/Gamma Spending model

gg_LL()

Gamma-Gamma: Log-Likelihood Function

ggomnbd(<clv.data>) ggomnbd(<clv.data.static.covariates>)

Gamma-Gompertz/NBD model

ggomnbd_nocov_CET() ggomnbd_staticcov_CET()

GGompertz/NBD: Conditional Expected Transactions

ggomnbd_nocov_LL_ind() ggomnbd_nocov_LL_sum() ggomnbd_staticcov_LL_ind() ggomnbd_staticcov_LL_sum()

GGompertz/NBD: Log-Likelihood functions

ggomnbd_staticcov_PAlive() ggomnbd_nocov_PAlive()

GGompertz/NBD: Probability of Being Alive

ggomnbd_nocov_PMF() ggomnbd_staticcov_PMF()

GGompertz/NBD: Probability Mass Function (PMF)

ggomnbd_nocov_expectation() ggomnbd_staticcov_expectation()

GGompertz/NBD: Unconditional Expectation

latentAttrition()

Formula Interface for Latent Attrition Models

lrtest()

Likelihood Ratio Test of Nested Models

newcustomer() newcustomer.static() newcustomer.dynamic()

New customer prediction data

nobs(<clv.data>)

Number of observations

nobs(<clv.fitted>)

Number of observations

plot(<clv.data>)

Plot Diagnostics for the Transaction data in a clv.data Object

plot(<clv.fitted.spending>)

Plot expected and actual mean spending per transaction

plot(<clv.fitted.transactions>)

Plot Diagnostics for a Fitted Transaction Model

pmf(<clv.fitted.transactions>)

Probability Mass Function

pnbd(<clv.data>) pnbd(<clv.data.static.covariates>) pnbd(<clv.data.dynamic.covariates>)

Pareto/NBD models

pnbd_nocov_CET() pnbd_staticcov_CET()

Pareto/NBD: Conditional Expected Transactions

pnbd_nocov_DERT() pnbd_staticcov_DERT()

Pareto/NBD: Discounted Expected Residual Transactions

pnbd_nocov_LL_ind() pnbd_nocov_LL_sum() pnbd_staticcov_LL_ind() pnbd_staticcov_LL_sum()

Pareto/NBD: Log-Likelihood functions

pnbd_nocov_PAlive() pnbd_staticcov_PAlive()

Pareto/NBD: Probability of Being Alive

pnbd_nocov_expectation() pnbd_staticcov_expectation()

Pareto/NBD: Unconditional Expectation

pnbd_nocov_PMF() pnbd_staticcov_PMF()

Pareto/NBD: Probability Mass Function (PMF)

predict(<clv.fitted.spending>)

Predict customers' future spending

predict(<clv.fitted.transactions>)

Predict CLV from a fitted transaction model

spending()

Formula Interface for Spending Models

subset(<clv.data>)

Subsetting clv.data

summary(<clv.fitted>) summary(<clv.fitted.transactions.static.cov>) print(<summary.clv.fitted>)

Summarizing a fitted CLV model

vcov(<clv.fitted>)

Calculate Variance-Covariance Matrix for CLV Models fitted with Maximum Likelihood Estimation