- DESCRIPTION file.
- Code demos. Use demo() to run them.

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

center | Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero. |

cor2adj | Threshold correlation matrix to match a given number of edges. |

corTs | Mean correlation of time series across subjects. |

dgm.group | A group is a list containing restructured data from subejcts for easier group analysis. |

diag.delta | Quick diagnostics on delta. |

dlm.lpl | Calculate the log predictive likelihood for a specified set of parents and a fixed delta. |

dlm.retro | Calculate the location and scale parameters for the time-varying coefficients given all the observations. West, M. & Harrison, J., 1997. Bayesian Forecasting and Dynamic Models. Springer New York. |

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

exhaustive.search | A function for an exhaustive search, calculates the optimum value of the discount factor. |

getAdjacency | Get adjacency and associated likelihoods (LPL) and disount factros (df) of winning models. |

getIncompleteNodes | Checks results and returns job number for incomplete nodes. |

getModel | Extract specific parent model with assocated df and ME from complete model space. |

getModelNr | Get model number from a set of parents. |

getWinner | Get winner network by maximazing log predictive likelihood (LPL) from a set of models. |

gplotMat | Plots network as adjacency matrix. |

mergeModels | Merges forward and backward model store. |

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 txt file. |

patel | Patel. |

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

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

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

prop.nettest | Comparing two population proportions on the network with FDR correction. |

pruning | Get pruned adjacency network. |

rand.test | Randomization test for Patel's kappa. Creates a distribution of values kappa under the null hypothesis. |

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

reshapeTs | Reshapes a 2D concatenated time series into 3D according to no. of subjects and volumes. |

rmdiag | Removes diagonal of NA's from matrix. |

rmna | Removes NAs from matrix. |

rmRecipLow | Removes reciprocal connections in the lower diagnoal of the network matrix. |

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

stepwise.backward | Stepise backward non-exhaustive greedy search, calculates the optimum value of the discount factor. |

stepwise.combine | Stepise combine |

stepwise.forward | Stepise forward non-exhaustive greedy search, calculates the optimum value of the discount factor. |

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

symmetric | Turns asymetric network into an symmetric network. Helper function to determine the detection of a connection while ignoring directionality. |

ttest.nettest | Comparing connectivity strenght of two groups with FDR correction. |

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