# Estimate the infectiousness of an information cascade

### Description

Estimate the infectiousness of an information cascade

### Usage

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

### Arguments

`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 |

### Details

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`

).

### Value

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

### Examples

1 2 3 4 |

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