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.
rmRecipLow              Removes reciprocal connections in the lower
                        diagnoal of the network matrix.
rmdiag                  Removes diagonal of NA's from matrix.
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
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.
