ergm.rank-package: Fit, Simulate and Diagnose Exponential-Family Models for...

ergm.rank-packageR Documentation

Fit, Simulate and Diagnose Exponential-Family Models for Rank-Order Relational Data

Description

ergm.rank is a set of extensions to package ergm to fit and simulate from exponential-family random graph models for networks whose edge weights are ranks. For a list of functions type help(package='ergm') and help(package='ergm.rank')

Details

Mainly, it implements the CompleteOrder reference measure for valued ERGMs (documented here), and provides some rank-order change statistics (documented here).

For a complete list of the functions, use library(help="ergm") and library(help="ergm.rank") or read the rest of the manual.

When publishing results obtained using this package, please cite the original authors as described in citation(package="ergm.rank").

All programs derived from this package must cite it.

This package contains functions specific to using ergm to model networks whose dyad values are ranks. Examples include preferences, valued ties reduced to ranks, etc..

These terms have a specialized interpretation, and are therefore generally prefixed by "rank.", though they should take any valued data.

For detailed information on how to download and install the software, go to the Statnet project website: https://statnet.org. A tutorial, support newsgroup, references and links to further resources are provided there.

Author(s)

Pavel N. Krivitsky pavel@statnet.org

References

Krivitsky PN (2012). Exponential-Family Random Graph Models for Valued Networks. Electronic Journal of Statistics, 2012, 6, 1100-1128. c("\Sexpr[results=rd,stage=build]tools:::Rd_expr_doi(\"#1\")", "doi:10.1214/12-EJS696")\Sexpr{tools:::Rd_expr_doi("doi:10.1214/12-EJS696")}

Krivitsky PN and Butts CT (2017). Exponential-Family Random Graph Models for Rank-Order Relational Data. Sociological Methodology, 2017, 47, 68-112. doi: 10.1177/0081175017692623

See Also

ergm-terms, ergm-references


ergm.rank documentation built on June 2, 2022, 1:06 a.m.