Simulate the number of glass fragments recovered given the conditions set by the user.

1 2 3 |

`N` |
Simulation size |

`d` |
The breaker's distance from the window |

`deffect` |
Distance effect. |

`lambda` |
The average number of glass fragments transferred to the breaker's clothing. |

`Q` |
Proportion of high persistence fragments. |

`l0` |
Lower bound on the percentage of fragments lost in the first hour |

`u0` |
Upper bound on the percentage of fragments lost in the first hour |

`lstar0` |
Lower bound on the percentage of high persistence fragments lost in the first hour |

`ustar0` |
Upper bound on the percentage of high persistence fragments lost in the first hour |

`lj` |
Lower bound on the percentage of fragments lost in the j'th hour |

`uj` |
Upper bound on the percentage of fragments lost in the j'th hour |

`lstarj` |
Lower bound on the percentage of high persistence fragments lost in the j'th hour |

`ustarj` |
Upper bound on the percentage of high persistence fragments lost in the j'th hour |

`lR` |
Lower bound on the percentage of fragments expected to be detected in the lab |

`uR` |
Upper bound on the percentage of fragments expected to be detected in the lab |

`t` |
Time between commission of crime and apprehension of suspect |

`r` |
Probability r in ti ~ NegBinom(t, r) |

`Y ` |
The simulated values of recovered glass fragments |

`para ` |
Input parameters |

James Curran and TingYu Huang

Curran, J. M., Hicks, T. N. & Buckleton, J. S. (2000). *Forensic
interpretation of glass evidence*. Boca Raton, FL: CRC Press.

Curran, J. M., Triggs, C. M., Buckleton, J. S., Walsh, K. A. J. &
Hicks T. N. (January, 1998). Assessing transfer probabilities in a
Bayesian interpretation of forensic glass evidence. *Science &
Justice*, *38*(1), 15-21.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
library(tfer)
## create a transfer object using default arguments
y = transfer()
## probability table
probs = tprob(y)
## extract the probabilities of recovering 8 to 15
## glass fragments given the user-specified arguments
tprob(y, 8:15)
## produce a summary table for a transfer object
summary(y)
## barplot of probabilities (default)
plot(y, ptype = 0)
plot(y)
## barplot of transfer frequencies
plot(y, ptype = 1)
## histogram
plot(y, ptype = 2)
``` |

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