The R package *econet* provides methods for estimating
parameter-dependent network centrality measures with linear-in-means
models. Both non linear 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 the econet
package are illustrated in the vignette of the package.

*econet* provides four functions. The first one is
*net_dep*, which allows one to estimate a model of social
interactions and compute the relative weighted Katz-Bonacich
centralities of the agents. Different behavioral models can be chosen
(see section 4 of the vignette for details). Moreover, the hypothesis of
homogenous or heterogenous spillovers can be tested. The second function
is *boot*, which is built to obtain valid inference when the NLLS
estimator with Heckman correction is used. The third function is
*horse_race*, which allows one to compare the explanatory power
of parameter-dependent centralities relative to other centrality
measures. The forth function is *quantify*, and it is used to
assess the effect of control variables in the framework designed by
BLP.

The package has at least four merits.

First, it complements the R packages implementing traditional
centrality measures for binary networks, *igraph* and
*sna*, and weighted networks, *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,
Pajek, Python and Stata.

The examples we use to showcase the functionality of *econet*
are contained in the vignette.