This package estimates the parameters of a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accomodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification.
|Date of publication||2012-03-23 21:45:14|
|Maintainer||Peter Hoff <firstname.lastname@example.org>|
addlines: Adds lines between nodes to an existing plot of nodes
eigenmodel_mcmc: Approximate the posterior distribution of parameters in an...
eigenmodel.package: Semiparametric factor and regression models for symmetric...
eigenmodel_setup: Setup constants and starting values for an eigenmodel fit
plot.eigenmodel_post: Plot the output of an eigenmodel fit
rb_fc: Sample from the full conditional distribution of the...
rmvnorm: Sample from the multivariate normal distribution
rUL_fc: Sample UL from its full conditional distribution
rZ_fc: Sample from the full conditional distribution of the probit...
ULU: Computes a matrix from its eigenvalue decomposition
XB: Computes a sociomatrix of regression effects
Y_Gen: Relations between words in the 1st chapter of Genesis
Y_impute: Impute missing values of a sociomatrix
Y_Pro: Butland's protein-protein interaction data
YX_Friend: Sex, race and friendship data from a 12th grade classroom