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Dear members of the list,
We are happy to announce that the R package "econet" for the
estimation of parameter-dependent centrality measures is now available
via CRAN ( https://urldefense.proofpoint.com/v2/url?u=https-3A__CRAN.R-2Dproject.org_package-3Deconet&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=v8fr8IJ69-HcQdAUSAlWydwuyIyoT2eMPfqzWJ0oF7I&s=bfrr9XFLbW-XnZ7WicLMO1OY_uYu8yH8WEqhPkfItQI&e= ).
econet provides methods for estimating parameter-dependent network
centrality measures with linear-in-means models. Both nonlinear least
squares and maximum likelihood estimators are implemented. The methods
allow for both link and node heterogeneity in network effects,
endogenous network formation and the presence of unconnected nodes.
The routines also compare the explanatory power of parameter-dependent
network centrality measures with those of standard measures of network
centrality. Benefits and features of econet are illustrated in the
vignette of the package, along with an extensive description of the
methodology and the relevant literature.
The package has at least four merits. First, it complements the R
packages implementing traditional centrality measures for binary
networks, e.g. igraph and sna, and weighted networks, e.g. tnet, by
introducing new eigensolutions-based techniques to rank agents'
centrality. Second, whereas previous packages, such as btergm, hergm,
the statnet suite, and xergm, created environments for modeling the
statistical processes underlying network formation, econet provides
the first framework to investigate the socio-economic processes
operating on networks (i.e. peer effects). Third, it completes the
collection of functions for modeling spatial dependence in
cross-sectional data provided by spdep and splm, by allowing the users
to: i) consider the presence of unconnected nodes, and ii) address
network endogeneity. Finally, it equips the R archive with routines
still unavailable in other commonly used software for the
investigation of relational data, such as Matlab, Python and Stata.
The examples contained in the vignette used to showcase the
functionality of econet are taken from Battaglini and Patacchini
(2018) and Battaglini, Leone Sciabolazza, and Patacchini (2018). The
vignette of the package is also available at
The authors of the package are:
- Marco Battaglini (Cornell University and EIEF)
- Valerio Leone Sciabolazza (University of Naples Parthenope)
- Eleonora Patacchini (Cornell University and EIEF)
- Sida Peng (Microsoft Research)
Feel free to contact me if you encounter any problems.
Valerio Leone Sciabolazza, Ph.D.
Department of Business and Economics
University of Naples Parthenope
E-mail: [log in to unmask]
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