Targeted Maximum Likelihood Estimation for Network Data

BinDat | R6 class for storing the design matrix and binary outcome for... |

BinOutModel | R6 class for fitting and making predictions for a single... |

CategorSummaryModel | R6 class for fitting and predicting joint probability for a... |

ContinSummaryModel | R6 class for fitting and predicting joint probability for a... |

DatNet | R6 class for storing and managing already evaluated summary... |

DatNet.sWsA | R6 class for storing and managing the combined summary... |

DefineSummariesClass | R6 class for parsing and evaluating user-specified summary... |

Define_sVar | R6 class for parsing and evaluating node R expressions. |

def.sW | Define Summary Measures sA and sW |

df_netKmax2 | An example of a row-dependent dataset with known network of... |

df_netKmax6 | An example of a row-dependent dataset with known network of... |

eval.summaries | Evaluate Summary Measures sA and sW |

mcEvalPsi | R6 class for Monte-Carlo evaluation of various substitution... |

NetInd_mat_Kmax6 | An example of a network ID matrix |

print_tmlenet_opts | Print Current Option Settings for 'tmlenet' |

RegressionClass | R6 class that defines regression models evaluating P(sA|sW),... |

SummariesModel | R6 class for fitting and predicting model P(sA|sW) under... |

tmlenet | Estimate Average Network Effects For Arbitrary (Stochastic)... |

tmlenet_options | Setting Options for 'tmlenet' |

tmlenet-package | Targeted Maximum Likelihood Estimation for Network Data |

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