Key | Value |
---|---|
FileName | ./usr/lib64/R/library/regressoR/R/regressoR.rdx |
FileSize | 3234 |
MD5 | F70353135126EAC147157E2B3293FAC7 |
SHA-1 | 27F70DC572A1C3968D03A41D6E8091D0369D9BC4 |
SHA-256 | 59319B2CC86813ACB60BE79F449941A784923578A1B782E08FCB95D761807778 |
SSDEEP | 48:XnFi9TZR3d6Tqv3WJuZB6boYRourfADMsSnkcEHJV0RG4GN0QdPPnMKTI1ptUr4W:CDN6+JZZurjotcRGzNPPMNmr4k63a3Jr |
TLSH | T1F1616C197D9D777944148C2DDACA9518D37A8DEA32E3CDB4F0C5B005CA4809D2EE8A71 |
hashlookup:parent-total | 3 |
hashlookup:trust | 65 |
The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
MD5 | 411117AC6ECC5429E11FAA49882BFF39 |
PackageArch | x86_64 |
PackageDescription | Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines. |
PackageName | R-regressoR |
PackageRelease | lp152.1.1 |
PackageVersion | 2.0.1 |
SHA-1 | D790ADC3EA87C2A9CDFD80DB379564250E434E18 |
SHA-256 | E821FED89F44020FDB9787A437A09BC348FF967DE1548D38DEB0642C81A63D53 |
Key | Value |
---|---|
MD5 | 66EB82D3D75EEE0D1F14992D9B28F860 |
PackageArch | x86_64 |
PackageDescription | Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines. |
PackageName | R-regressoR |
PackageRelease | lp153.1.1 |
PackageVersion | 2.0.1 |
SHA-1 | 6EBF19B40D9944CF4F2D7965FF83C2561FE6C486 |
SHA-256 | 68EE6F69FA3B460CCA3BFE5D1CAC7817232A4EFA2BB3A146F31A13A4C67F4BE9 |
Key | Value |
---|---|
MD5 | BD5F3932A816F45F639A2174E0C009CC |
PackageArch | x86_64 |
PackageDescription | Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines. |
PackageName | R-regressoR |
PackageRelease | lp154.1.1 |
PackageVersion | 2.0.1 |
SHA-1 | EC52D06F3414F5B3FDE78A8E5AF2D1D79DA42538 |
SHA-256 | ED116C9F7084BF73B41A95FD3B04EAD7AD4523C39DF2E811E9B6D1DF8EDDEA6C |