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
apparelDynCovFuture     Future 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                    BG/BB models - Work In Progress
bgnbd                   BG/NBD models
bgnbd_CET               BG/NBD: Conditional Expected Transactions
bgnbd_LL                BG/NBD: Log-Likelihood functions
bgnbd_PAlive            BG/NBD: Probability of Being Alive
bgnbd_expectation       BG/NBD: Unconditional Expectation
bgnbd_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                      Gamma/Gamma Spending model
gg_LL                   Gamma-Gamma: Log-Likelihood Function
ggomnbd                 Gamma-Gompertz/NBD model
ggomnbd_CET             GGompertz/NBD: Conditional Expected
                        Transactions
ggomnbd_LL              GGompertz/NBD: Log-Likelihood functions
ggomnbd_PAlive          GGompertz/NBD: Probability of Being Alive
ggomnbd_PMF             GGompertz/NBD: Probability Mass Function (PMF)
ggomnbd_expectation     GGompertz/NBD: Unconditional Expectation
hessian                 Calculate hessian for a fitted model
latentAttrition         Formula Interface for Latent Attrition Models
lrtest                  Likelihood Ratio Test of Nested Models
newcustomer             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                     Probability Mass Function
pnbd                    Pareto/NBD models
pnbd_CET                Pareto/NBD: Conditional Expected Transactions
pnbd_DERT               Pareto/NBD: Discounted Expected Residual
                        Transactions
pnbd_LL                 Pareto/NBD: Log-Likelihood functions
pnbd_PAlive             Pareto/NBD: Probability of Being Alive
pnbd_expectation        Pareto/NBD: Unconditional Expectation
pnbd_pmf                Pareto/NBD: Probability Mass Function (PMF)
predict.clv.fitted.spending
                        Infer customers' 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      Summarizing a fitted CLV model
vcov.clv.fitted         Calculate Variance-Covariance Matrix for CLV
                        Models fitted with Maximum Likelihood
                        Estimation
