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| binom.nettest | Performes a binomial test with FDR correction for network edges in an adjacency matrix. |
| center | Mean centers timeseries in a 2D array timeseries x nodes, i.e. each timeseries of each node has mean of zero. |
| corTs | Correlation of time series. |
| dlm.lpl | Calculate the log predictive likelihood for a specified set of parents and a fixed delta. |
| 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. |
| getModel | Get specific parent model from all models. |
| getThreshAdj | Get thresholded adjacency network. |
| getWinner | Get winner network by maximazing log predictive likelihood (LPL) from a set of models. |
| gplotMat | Plots network as adjacency matrix. |
| mdm.group | A group is a list containing restructured data from subejcts for easier group analysis. |
| 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. |
| perm.test | Permutation 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. |
| scaleTs | Scaling data. Zero centers and scales the nodes (SD=1). |
| subject | Estimate subject's full network: runs exhaustive search on very node. |
| utestdata | Results from v.1.0 for unit tests. |