Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	68101C092A171C5B50AE5E4C669F4E14
PackageArch	x86_64
PackageDescription	An 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.
PackageName	R-ELMSO
PackageRelease	lp153.2.3
PackageVersion	1.0.1
SHA-1	4B93F4F038D9BC32E02F6C87BBD38B17618F6B3E
SHA-256	1416D2426189C114C288DED6F733F7FE4DEECD638F3275686224D91B746F4943

Key	Value
MD5	D95A7A12DACF22B03D70E113354F50C1
PackageArch	x86_64
PackageDescription	An 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.
PackageName	R-ELMSO
PackageRelease	lp153.2.1
PackageVersion	1.0.1
SHA-1	100BF97C0EB2DA6D3BF2A63A903E8B57497D1B98
SHA-256	A7937B7163BB93D2C5AF95C968AD00587617C833806F8D953D3C7A741B4EE778

Key	Value
MD5	86CDB60703BCBA4A1E67B68986D0A1A6
PackageArch	x86_64
PackageDescription	An 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.
PackageMaintainer	https://www.suse.com/
PackageName	R-ELMSO
PackageRelease	lp154.2.1
PackageVersion	1.0.1
SHA-1	8719E1D28831FE704C5ED4B4A6B07B8A91FA7E80
SHA-256	89E84239E5DFDEB422334EB924E20EA06AA6226EBA14C8F19D64F14A6BA84E9A

Key	Value
MD5	8DC5D211A05E1CF1E9CA248B00FF5048
PackageArch	x86_64
PackageDescription	An 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.
PackageName	R-ELMSO
PackageRelease	2.21
PackageVersion	1.0.1
SHA-1	790F81FDE4A16BB742973A1F1BF029359F6412F2
SHA-256	20D944660326116F8A696563EF9D033E392D0041FDCF076649D46E8A6B4AA9F0

Key	Value
MD5	D97F204FFC5051A0DB3AD27B6129847F
PackageArch	x86_64
PackageDescription	An 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.
PackageName	R-ELMSO
PackageRelease	lp152.2.6
PackageVersion	1.0.1
SHA-1	E6D1DAE6FBACCB930AC7706E62B4B7C99E230E50
SHA-256	A0D887397F5115DED96DBF5AF18AAF39A39807A846EC3B46F59FDFEBFA5A27EE