{ "_id": "6a1d48ce1d7bb097a0a42b2f", "Package": "econet", "Type": "Package", "Title": "Estimation of Parameter-Dependent Network Centrality Measures", "Version": "1.0.0.1", "Authors@R": "c(person(given = \"Marco\", family = \"Battaglini\", role = \"aut\", email = \"battaglini@cornell.edu\", comment = c(ORCID = \"https://orcid.org/0000-0001-9690-0721\")), person(given = \"Valerio\", family = \"Leone Sciabolazza\", role = c(\"aut\", \"cre\"), email = \"valerio.leonesciabolazza@uniroma1.it\", comment = c(ORCID = \"https://orcid.org/0000-0003-2537-3084\")), person(given = \"Eleonora\", family = \"Patacchini\", role = \"aut\", email = \"ep454@cornell.edu\", comment = c(ORCID = \"https://orcid.org/0000-0002-3510-2969\")), person(given = \"Sida\", family = \"Peng\", role = \"aut\", email = \"sidpeng@microsoft.com\", comment = c(ORCID = \"https://orcid.org/0000-0002-2151-0523\")))", "Description": "Provides methods for estimating parameter-dependent\nnetwork centrality measures with linear-in-means models. Both\nnon linear least squares and maximum likelihood estimators are\nimplemented. The methods allow for both link and node\nheterogeneity in network effects, endogenous network formation\nand the presence of unconnected nodes. The routines also\ncompare the explanatory power of parameter-dependent network\ncentrality measures with those of standard measures of network\ncentrality. Benefits and features of the 'econet' package are\nillustrated using data from Battaglini and Patacchini (2018)\nand Battaglini, Patacchini, and Leone Sciabolazza (2020). 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