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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  
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.valerioleonesciabolazza.com_rpackages_econet_&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=v8fr8IJ69-HcQdAUSAlWydwuyIyoT2eMPfqzWJ0oF7I&s=1ghr4_BMSjSfOF8Hm2VzWnoDuj1Mpaf_BCk18Ybq3CA&e=

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.
Best regards,
Valerio Leone Sciabolazza, Ph.D.
Department of Business and Economics
University of Naples Parthenope
E-mail: [log in to unmask]
Skype: sciabolazza
Website: www.valerioleonesciabolazza.com

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