Function reference • CLVTools

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
`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_expectation()` `ggomnbd_staticcov_expectation()`	GGompertz/NBD: Unconditional Expectation
`latentAttrition()`	Formula Interface for Latent Attrition Models
`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(<clv.fitted.spending>)`	Plot expected and actual mean spending per transaction
`plot(<clv.fitted.transactions>)` `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(<clv.fitted.spending>)`	Predict customers' future spending
`predict(<clv.fitted.transactions>)` `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

Reference

All functions