| Key | Value |
|---|---|
| FileName | ./usr/lib64/R/library/econet/R/econet.rdx |
| FileSize | 1196 |
| MD5 | F71B2A6C7CE282721AE7961AAAC126C2 |
| SHA-1 | 05EE208FEF585104E4806F8EC9B309CC148AEE2D |
| SHA-256 | 06778B9EB4A21B0B8CE5A8C8ABD9DE4EDB9C435E80CEED85C416589584EC56FB |
| SSDEEP | 24:XgUSKi9/O7LivkO1Yi9LvOWYcYFu5goojMVqyLgVmXqBQB/KqbS:Xh5s/zvksY2LxWuIiGuqI/VS |
| TLSH | T1BA21DA025D95FA4280FFC6B509FFEEF12E1A16C46925960F0CF4EA34512B99A2E4B50C |
| hashlookup:parent-total | 2 |
| hashlookup:trust | 60 |
The searched file hash is included in 2 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 | 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 |