Calculates the expected number of transactions in a given time period based on a customer's past transaction behavior and the GGompertz/NBD model parameters.

`ggomnbd_nocov_CET`

Conditional Expected Transactions without covariates

`ggomnbd_staticcov_CET`

Conditional Expected Transactions with static covariates

```
ggomnbd_nocov_CET(r, alpha_0, b, s, beta_0, dPeriods, vX, vT_x, vT_cal)
ggomnbd_staticcov_CET(
r,
alpha_0,
b,
s,
beta_0,
dPeriods,
vX,
vT_x,
vT_cal,
vCovParams_trans,
vCovParams_life,
mCov_life,
mCov_trans
)
```

- r
shape parameter of the Gamma distribution of the purchase process. The smaller r, the stronger the heterogeneity of the purchase process.

- alpha_0
scale parameter of the Gamma distribution of the purchase process.

- b
scale parameter of the Gompertz distribution (constant across customers)

- s
shape parameter of the Gamma distribution for the lifetime process The smaller s, the stronger the heterogeneity of customer lifetimes.

- beta_0
scale parameter for the Gamma distribution for the lifetime process

- dPeriods
number of periods to predict

- vX
Frequency vector of length n counting the numbers of purchases.

- vT_x
Recency vector of length n.

- vT_cal
Vector of length n indicating the total number of periods of observation.

- vCovParams_trans
Vector of estimated parameters for the transaction covariates.

- vCovParams_life
Vector of estimated parameters for the lifetime covariates.

- mCov_life
Matrix containing the covariates data affecting the lifetime process. One column for each covariate.

- mCov_trans
Matrix containing the covariates data affecting the transaction process. One column for each covariate.

Returns a vector containing the conditional expected transactions for the existing customers in the GGompertz/NBD model.

`mCov_trans`

is a matrix containing the covariates data of
the time-invariant covariates that affect the transaction process.
Each column represents a different covariate. For every column a gamma parameter
needs to added to `vCovParams_trans`

at the respective position.

`mCov_life`

is a matrix containing the covariates data of
the time-invariant covariates that affect the lifetime process.
Each column represents a different covariate. For every column a gamma parameter
needs to added to `vCovParams_life`

at the respective position.

Bemmaor AC, Glady N (2012). “Modeling Purchasing Behavior with Sudden “Death”: A Flexible Customer Lifetime Model” Management Science, 58(5), 1012-1021.