Result for 186BFCC9B8A744575BC8DC0730A886F148140A4F

Query result

Key Value
FileName./usr/lib/R/site-library/spdep/Meta/package.rds
FileSize3094
MD56666D68DDB4D616D649192EB336CB5B9
SHA-1186BFCC9B8A744575BC8DC0730A886F148140A4F
SHA-25612456D3325B3B2EA4984790ED9C5C1C6FBD183F332697DC4878909156466D6CC
SSDEEP48:XjBjUo49tdeGGeVvM2LUfrwcj67A/O+XIx9bGn4uNdt4tJ8Fd+ciXwbQa9nkjM1G:tAoiALe9nIfr5j6U94xkJdy3sDnkj+G
TLSHT175513DE67435C46615A9EBD6973B97D545317A390C14824F04537B3CFB04EC7888DBC8
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
FileSize2007020
MD51044EFA48C0C2F8EF69577C54534A6FA
PackageDescriptionGNU R spatial dependence: weighting schemes, statistics and models A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial autocorrelation, including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross product statistic, Empirical Bayes estimates and Assunção/Reis Index, Getis/Ord G and multicoloured join count statistics, local Moran's I and Getis/Ord G, saddlepoint approximations and exact tests for global and local Moran's I; and functions for estimating spatial simultaneous autoregressive (SAR) lag and error models, impact measures for lag models, weighted and unweighted SAR and CAR spatial regression models, semi-parametric and Moran eigenvector spatial filtering, GM SAR error models, and generalized spatial two stage least squares models.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-spdep
PackageSectiongnu-r
PackageVersion1.1-3+dfsg-1
SHA-1FBFAD1956A8D415F6A1A76114CF173A8E07D0A9A
SHA-2566AB0CC1C108E64216C38124C4477D6D7F0978B93DE06D6F93266C8E9ACFAC4C4