Regression of Network Responses
build list of time blocks that are correlated based on the maximum tim...
Build an exchangeable matrix of sparseMatrix class
Build intermediate C(phi,n) matrix in inversion of Exchangeable varian...
calculate parameter estimates for different types of matrices, i.e. 6a...
Invert matrix parameters based on inputs.
Coef S3 generic for class lmnet
Find all possible combinations of elements in two vectors, or all comb...
Dyad map from nodes i,j --> dyad d
Eigenvalues of exchangeable matrices if calcall == TRUE, then output e...
Compute eigenvalues of covariance matrices of jointly exchangeable err...
Perform GEE estimate / IRWLS of coefficients
Perform GEE estimate / IRWLS of coefficients for temporal data
Input preprocessing
Invert an exchangeable matrix
Linear regression for network response
Replace negative eigenvalues with zeros in variance matrix
Matricize a network vector (without diagonal)
Calculate DC meat using rows of X, e
Calculate E meat using rows of X, e
Matrix product of A^TBC where B is a short list of parameters A and C ...
model.matrix S3 generic for class lmnet
Generate node pairs for complete network
Generate node sets of various overlapping dyad pairs
Make complete node indices for temporal relational data
Pre-processes data for ordering etc.
Pre-processes data for ordering, FOR TEMPORAL DATA, etc.
Calculate parameter estimates using rows of e
Given matrix of time blocks and a particular exchangeable parameter se...
Plot S3 generic for class lmnet
Print S3 generic for class lmnet
Print S3 generic for class summary.lmnet
Print S3 generic for summary.vnet object
Print S3 generic for vnet object
Generate row list based on nodes input with missingness
Make row list for complete temporal relational data
Generate positive definite phi set
Generate list indicator matrix of overlapping dyads
Summary S3 generic for class lmnet
Summary S3 generic for vnet object
Compute symmetric square root of A, assuming it is real, symmetric, po...
vcov S3 generic for class lmnet
Vectorize a network matrix (without diagonal)
Variance computation for linear regression of network response
Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.