Result for 21CCA88E32A12C1AF0B491E26BCCD3B32961BDB9

Query result

Key Value
FileName./usr/lib64/R/library/ELMSO/help/ELMSO.rdb
FileSize8492
MD5167AFFFDE3A12E844F2509D839935301
SHA-121CCA88E32A12C1AF0B491E26BCCD3B32961BDB9
SHA-256D1C18B69AE98D858BD80B2BC50A9BED81DF380567DC68C127829F4805AD980D1
SSDEEP192:0NnvKzmzxw5CghRFk0DLUkc+joaxdcVEOn1tJNdb:0xyzmzxzgZk0/fca3cucJb
TLSHT168028D18374047D1B838634763B90B8BB1EA466EF0D127B93043ACED789AC4DD9B1E9C
hashlookup:parent-total4
hashlookup:trust70

Network graph view

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
MD568101C092A171C5B50AE5E4C669F4E14
PackageArchx86_64
PackageDescriptionAn implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.
PackageNameR-ELMSO
PackageReleaselp153.2.3
PackageVersion1.0.1
SHA-14B93F4F038D9BC32E02F6C87BBD38B17618F6B3E
SHA-2561416D2426189C114C288DED6F733F7FE4DEECD638F3275686224D91B746F4943
Key Value
MD58DC5D211A05E1CF1E9CA248B00FF5048
PackageArchx86_64
PackageDescriptionAn implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.
PackageNameR-ELMSO
PackageRelease2.21
PackageVersion1.0.1
SHA-1790F81FDE4A16BB742973A1F1BF029359F6412F2
SHA-25620D944660326116F8A696563EF9D033E392D0041FDCF076649D46E8A6B4AA9F0
Key Value
MD5D97F204FFC5051A0DB3AD27B6129847F
PackageArchx86_64
PackageDescriptionAn implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.
PackageNameR-ELMSO
PackageReleaselp152.2.6
PackageVersion1.0.1
SHA-1E6D1DAE6FBACCB930AC7706E62B4B7C99E230E50
SHA-256A0D887397F5115DED96DBF5AF18AAF39A39807A846EC3B46F59FDFEBFA5A27EE
Key Value
MD586CDB60703BCBA4A1E67B68986D0A1A6
PackageArchx86_64
PackageDescriptionAn implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.
PackageMaintainerhttps://www.suse.com/
PackageNameR-ELMSO
PackageReleaselp154.2.1
PackageVersion1.0.1
SHA-18719E1D28831FE704C5ED4B4A6B07B8A91FA7E80
SHA-25689E84239E5DFDEB422334EB924E20EA06AA6226EBA14C8F19D64F14A6BA84E9A