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.
corTs                   Correlation of time series.
dlm.lpl                 Calculate the log predictive likelihood for a
                        specified set of parents and a fixed delta.
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.
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.
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 no. of subjects and volumes.
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 optimum value of the discount
                        factor.
stepwise.combine        Stepise combine: combines the stepwise forward
                        and the stepwise backward model.
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.
utestdata               Results from v.1.0 for unit tests.
