Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	5626D3A60E38201F0276E8C2EAC52442
PackageArch	x86_64
PackageDescription	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 using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
PackageName	R-econet
PackageRelease	lp153.2.2
PackageVersion	0.1.94
SHA-1	87A376379965CC56E913613670F1BC2811366E49
SHA-256	D4B5486BFAB5BE1BBE8F96DE06AC67C3F4D435A2E99B7A8B43EE0039CA4ABEF4

Key	Value
MD5	48D6F0EF1AE8E561F7F5B6540BBAC1D1
PackageArch	x86_64
PackageDescription	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 using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
PackageName	R-econet
PackageRelease	lp154.2.1
PackageVersion	0.1.94
SHA-1	000972A1F806D67429B5F164A146276B75851B14
SHA-256	A5E5EC71E68CAE815B5759BD8E3B356C74736597C2E9F5DAD2DE93F9D71BAF7A

Key	Value
MD5	5425CCD01CDD498FE0A736C44788154B
PackageArch	x86_64
PackageDescription	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 using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
PackageName	R-econet
PackageRelease	lp152.2.3
PackageVersion	0.1.94
SHA-1	62D0706A2EA927B080BA992E8CBF02B1AB1B62A4
SHA-256	F551D294F03192EAC2D114E70917F42583DF0A5BDC608D093A36925B3D36BCDC

Key	Value
MD5	F4AE731DCB699AC5F97832067473273A
PackageArch	x86_64
PackageDescription	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 using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
PackageName	R-econet
PackageRelease	2.16
PackageVersion	0.1.94
SHA-1	C6EF9AC0723B8F5153EDC9521A85F39F2CA3035D
SHA-256	76935B36C1ADDDDBC3BBCFE931CAD0EE3EC5CCDCCFA3489F9CE51C9DB47B5847