Result for 2D331BC4CC44D5BC7E2747F7E399D3BACAC5A48F

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Parent (70)

Query result

Key Value
FileNameusr/lib/python3.12/site-packages/emcee-3.1.6.dist-info/top_level.txt
FileSize6
MD5FE2F15284A8282CED4D61BD7E7A62B0E
SHA-12D331BC4CC44D5BC7E2747F7E399D3BACAC5A48F
SHA-25660D151B6BD925FF6493E1C99C514E41E26996BA53469EA4402CF717943F08783
SSDEEP3:tG1:w
TLSH
tar:gnameroot
tar:unameroot
hashlookup:parent-total70
hashlookup:trust100

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Parents (Total: 70)

The searched file hash is included in 70 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
MD549B25D4F0A5F71B0FD3A7A4793FBD9E3
PackageArchnoarch
PackageDescription emcee 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.
PackageMaintainerFedora Project
PackageNamepython-emcee
PackageRelease1.fc21
PackageVersion2.1.0
SHA-1010B5EC003E5C8D7150C20C22E1E6D3D56C3F41C
SHA-256F2357D3FF6FA4826CB51F69F2802A27963241F11F6ED5236F0659BEC88914A5D
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
MD5767CD93A1EC92167E45E132C76566B0B
PackageArchnoarch
PackageDescription emcee 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.
PackageMaintainerFedora Project
PackageNamepython3-emcee
PackageRelease5.fc33
PackageVersion3.0.0
SHA-1167664F6BD8C0380F074E2BA09EC1248D27BFFBF
SHA-2562B1180E638B3CBCC590F27E8A0870704BF7C13EC703733AEF5AE1116550163E5
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





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