Multiregression Dynamic Models

binom.nettest | Performes a binomial test with FDR correction for network... |

center | Mean centers timeseries in a 2D array timeseries x nodes,... |

corTs | Correlation of time series. |

dlm.lpl | Calculate the log predictive likelihood for a specified set... |

dlmLplCpp | C++ implementation of the dlm.lpl |

exhaustive.search | A function for an exhaustive search, calculates the optimum... |

getAdjacency | Get adjacency and associated likelihoods (LPL) and disount... |

getModel | Get specific parent model from all models. |

getThreshAdj | Get thresholded adjacency network. |

getWinner | Get winner network by maximazing log predictive likelihood... |

gplotMat | Plots network as adjacency matrix. |

mdm.group | A group is a list containing restructured data from subejcts... |

model.generator | A function to generate all the possible models. |

myts | Network simulation data. |

node | Runs exhaustive search on a single node and saves results in... |

patel | Patel. |

patel.group | A group is a list containing restructured data from subejcts... |

perf | Performance of estimates, such as sensitivity, specificity,... |

perm.test | Permutation test for Patel's kappa. Creates a distribution of... |

priors.spec | Specify the priors. Without inputs, defaults will be used. |

read.subject | Reads single subject's network from txt files. |

reshapeTs | Reshapes a 2D concatenated time series into 3D according to... |

rmdiag | Removes diagnoal from matrix with NAs. |

rmna | Removes NAs from matrix. |

scaleTs | Scaling data. Zero centers and scales the nodes (SD=1). |

stepwise.backward | Stepise backward non-exhaustive greedy search, calculates the... |

stepwise.combine | Stepise combine: combines the stepwise forward and the... |

stepwise.forward | Stepise forward non-exhaustive greedy search, calculates the... |

subject | Estimate subject's full network: runs exhaustive search on... |

utestdata | Results from v.1.0 for unit tests. |

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