Description Usage Arguments Details Value

This function is to estimate the power values given fixed proportion r for each sub-population, which we utilize Monte Carlo method and GPU accelerator to estimate the power value. The user can specify the standard deviation and harzard reduction for each sub-population as the prior information of harzard reduction distribution, when not specified, we apply a default setting of linear harzard reduction scheme and the sd for each sub-population is inversely proportional to sqrt(r_i)

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`r` |
vector for the proportion for each sub-population, r_1is 1, r_i>r_i+1 |

`N1` |
integer, which is fixed as 10240 in our package |

`N2` |
integer, which is fixed as 20480 in our package |

`N3` |
integer, the number of grid point for the sig.lv, which should be the multiples of 5, because we apply 5 stream parallel |

`E` |
integer, the total number of events for the Phase 3 clinical trail, if not specified, then an estimation will be applied |

`sig` |
the vector of standard deviation of each sub-population |

`sd_full` |
a numeric number, which denotes the prior information of standard deviation for the harzard reduction. If sig is not specified, then sd_full must has an input value to define the standard deviation of the full population |

`delta` |
vector, the point estimation of harzard reduction in prior information, if not specified we apply a linear scheme by giving bound to the linear harzard reduction |

`delta_linear_bd` |
vector of length 2, specifying the upper bound and lower bound for the harzard reduction; if user don't specify the delta for each sub-population, then the linear scheme will apply and the input is a must. |

`seed` |
integer, seed for random number generation |

We interface python by reticulate package to utilize numba(cuda version) module to accelerate calculation.

list of 2 parts of the sampling points given specific r; alpha is the matrix as each row is the given sig.lv for each population; power is the corresponding power values given each row of the alpha

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