Estimate the infectiousness of an information cascade

1 2 | ```
get.infectiousness(share.time, degree, p.time, max.window = 2 * 60 * 60,
min.window = 300, min.count = 5)
``` |

`share.time` |
observed resharing times, sorted, share.time[1] =0 |

`degree` |
observed node degrees |

`p.time` |
equally spaced vector of time to estimate the infectiousness, p.time[1]=0 |

`max.window` |
maximum span of the locally weight kernel |

`min.window` |
minimum span of the locally weight kernel |

`min.count` |
the minimum number of resharings included in the window |

Use a triangular kernel with shape changing over time. At time p.time, use a triangluer kernel with slope = min(max(1/(`p.time`

/2), 1/`min.window`

), `max.window`

).

a list of three vectors:

infectiousness. the estimated infectiousness

p.up. the upper 95 percent approximate confidence interval

p.low. the lower 95 percent approximate confidence interval

1 2 3 4 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.