Multiregression Dynamic Models

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Documentation for package ‘multdyn’ version 1.6

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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.