| df_aggr | data.frame | | data.frame with the observed number of conversions and touchpoints. |
| type | str | "reward" | if "reward" then reward model is used for the attribution with the aggregated data while if "auto_mmm" then auto_mmm function is used. |
| df_ctr | data.frame | None | data.frame with click-through rates for channels expressed in number of clicks |
| df_paths | data.frame | None | data.frame with customer journeys |
| channel_conv_name | str | "((CONV))" | how conversion state is identified in df_paths |
| order | int | 1 | Markov model order |
| nsim_start | int | 1e5 | minimum number of simulations to be used in computation |
| max_step | int | None | maximum number of length for a single simulated path |
| ncore | int | 1 | number of threads to be used in computation |
| nfold | int | 10 | how many repetitions to be used to verify if convergence has been reached at each iteration |
| seed | int | 1234567 | random seed. Giving this parameter the same value over different runs guarantees that results will not vary |
| conv_par | double | 0.05 | convergence parameter for the algorithm. The estimation process ends when the percentage of variation of the results over different repetions is less than convergence parameter |
| rate_step_sim | double | 1.5 | number of simulations used at each iteration is equal to the number of simulations used at previous iteration multiplied by rate_step_sim |
| verbose | bool | True | if True, additional information during the execution will be shown |
| server | str | "app.channelattribution.io" | address of the server where password will be checked to authorize the execution of the function |
| password | str | None | user |