Result for 3DCA1DAB6B9A11DFE80C3EE3B3F1C5ED12B09DF9

Query result

Key Value
FileName./usr/lib64/R/library/econet/DESCRIPTION
FileSize2376
MD5193CD6581841B8C16BF7BC3D9709037B
SHA-13DCA1DAB6B9A11DFE80C3EE3B3F1C5ED12B09DF9
SHA-256450F008DDA0C9823A7D123030D89ADAB9F01D66443AAC6D0D33409C302732B94
SSDEEP48:C5XO2Lgc8BbLBnuMAnPHNci9NJHAqC5ULQxf15fSzFnsRFU+0Dxsa:Cc2Lb8BbLBnuMAPSi9rNCCQxd5axyZ0L
TLSHT15941A47322915249774301E27D527201BB5FF32E2AD304ECA81883F4230A9F48AEB758
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD548D6F0EF1AE8E561F7F5B6540BBAC1D1
PackageArchx86_64
PackageDescriptionProvides 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.
PackageNameR-econet
PackageReleaselp154.2.1
PackageVersion0.1.94
SHA-1000972A1F806D67429B5F164A146276B75851B14
SHA-256A5E5EC71E68CAE815B5759BD8E3B356C74736597C2E9F5DAD2DE93F9D71BAF7A