| Data | data.frame | | data.frame containing time-series of aggregated traffic data from different channels, aggregated conversions and/or conversion value |
| var_time | str | | name of the column containing time dimension |
| var_conv | str | | name of the column containing aggregated conversions |
| channels | lis(str) | list of names of the columns containing aggregated traffic data for channels considered | |
| var_value | str | None | name of the column containing aggregated conversion value |
| glob_conv_rate | double | None | global conversion rate for the use case considered. If None, it will be internally estimated |
| L_conv_rate | list(double) | None | conversion rate for each channel considered. If None, then the global conversion rate will be used for each channel considered |
| L_weight | double/list(double) | 1.0 | weight for each channel considered. It is a quantity between 0 and 1 and it is used to weigh differently different types of touches (e.g. clicks and impressions) |
| mean_cj_length | double | 1.0 | It indicates how many units of time are long the customer journeys on average for the use case considered. E.g., if input data are weekly and the parameter is set to 1.5, it means that customer journeys are long 1 week and a half on average for the use case considered |
| L_ads_rate | double/list(double) | 0.0 | list of adstock rates (between 0 and 1) for the channels considered |
| nsim | int | 10 | number of simulations to be performed |
| seed | int | 1234567 | random seed. Giving this parameter the same value over different runs guarantees that results will not vary |
| 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 |