Result for 430000B49B7386DCFC50C626620DBE700AF7C734

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Parent (43)

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Key Value
FileNameusr/lib/python3.12/site-packages/emcee/__init__.py
FileSize846
MD5F7A19DF59716223FBAD68A715A7B482B
SHA-1430000B49B7386DCFC50C626620DBE700AF7C734
SHA-256819515DA9548893047C9288AF41E05631FC77EA88F181FCD129A4C700AED2125
SSDEEP24:l7ePxPPvP7y5/PTg+3GZQGEvOMqoZmBKPvbV9ewggSY:RePxPDy2+2ZQGCOMXZ6KXbV9ewgzY
TLSHT18901C22BF5246F318F79018518E941131AB9013F7DD43C50BBA9931E5F1E93F98F9948
tar:gnameroot
tar:unameroot
hashlookup:parent-total43
hashlookup:trust100

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Parents (Total: 43)

The searched file hash is included in 43 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize27284
MD5841C69E63AC761E7318305521AF6E1D8
PackageDescriptionAffine-invariant ensemble MCMC sampling for Python 3 emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion3.0.2-2
SHA-1006FF24CE3B43A21DC9C55BD791E81C55650B6A1
SHA-25602B993A48F195D43CFC444060880A87C4A62D68EA343A816F53ECE0FBC0F1482
Key Value
MD503E1F0C041D1CB4B738CE9656FDB92EB
PackageArchnoarch
PackageDescriptionEmcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature.
PackageMaintainerghibo <ghibo>
PackageNamepython3-emcee
PackageRelease1.mga9
PackageVersion3.1.3
SHA-100D6403585F9B1EF62326C20CDFADAF86894DC67
SHA-2569AA97DFBED62C986C7BF782C4B4DF4B600D5D4F31B2E03377FA29ECD90661255
Key Value
MD5576D0BF963CF00BC6D9A35169B3DC2D6
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageRelease2.1
PackageVersion3.0.2
SHA-10804327E988AC64A392B51184765EA0D79F01010
SHA-256D8A45DCD577FF84FE07CE4BBFFC324531C3045C248E5AF78B367AAC1ED208321
Key Value
MD506A7C768308BB5F4EA3A2BDA9960946D
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython38-emcee
PackageRelease7.10
PackageVersion3.1.1
SHA-10B83C84B987968ADFE53508D88E3B920807BCD9D
SHA-2561BA7BFDF31C878A77335B985A224122CE37151EE16B3443B5203628B3ED7D07A
Key Value
MD5387069683E9154BE807FBC9446C67658
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython39-emcee
PackageRelease1.1
PackageVersion3.1.0
SHA-112298657739E619DF5C7E0337ACFD9FAF0093138
SHA-2566DA021854063D785C086F782084E011877FDDAF69968E5C004626E3F4901E778
Key Value
MD5BBDC17C6B5F997BABC737139766B846E
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageReleaselp152.3.2
PackageVersion3.0.2
SHA-113D212065A9E7F7478C7530CECFB28D4A7D109EF
SHA-2569BD30DB35FF52D39974D1E279860E68C33873E1CB09E10FD0C252B0A1BB5AC9D
Key Value
FileSize28804
MD56BF3059A1F478DE005F39794366850C4
PackageDescriptionAffine-invariant ensemble MCMC sampling for Python 3 emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation.
PackageMaintainerDebian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion3.0.2-2
SHA-117986342F25FF116DC62559DE43D1877E085A237
SHA-25676562CE931F850D729942D31AF72434BE3B8D8D1A0731162D16041F018A25B43
Key Value
MD5DFA9691B2D38D4CF783A0476876D5D8B
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython38-emcee
PackageRelease1.2
PackageVersion3.1.1
SHA-11ADB56B575FD15742109771FE6619FC0510B4F46
SHA-256E12BAA04B4EF0A16620DF2BE87562912304FE4203EC67141C483C73C6C210476
Key Value
MD5AD964B64831F212D3B01875019EB9C81
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython39-emcee
PackageRelease7.12
PackageVersion3.1.1
SHA-1254A60D826B0452AA9EE299DB3156CED3849758A
SHA-2568F9A0B28EC754E28D63E893C940B4FB63EC39C2EBE6B1711281EDC71E87B1E98
Key Value
FileNamehttp://dl-cdn.alpinelinux.org/alpine/latest-stable//community//x86_64//py3-emcee-3.1.6-r0.apk
MD58D3832B2C98FBB1EDF59126CD1F45031
SHA-1299FE8EA8CF70F2F2C3595D6900E6A5CD689679A
SHA-256B15328E9C470C2C51D15EEF442E586C909EC8F79BA64172ABA3436748BCC7940
SSDEEP768:ixOK9OIb0MvKRdfowYkrSY9OGA4lZfnPd:0OK3JKnthAAfn1
TLSHT190D2F17A45001666B0FF80A6E1710CBE50694AD675AAB3F3653D16B203393CD0CE3BAF





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