Parents (Total: 22)

The searched file hash is included in 22 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	03E1F0C041D1CB4B738CE9656FDB92EB
PackageArch	noarch
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.
PackageMaintainer	ghibo <ghibo>
PackageName	python3-emcee
PackageRelease	1.mga9
PackageVersion	3.1.3
SHA-1	00D6403585F9B1EF62326C20CDFADAF86894DC67
SHA-256	9AA97DFBED62C986C7BF782C4B4DF4B600D5D4F31B2E03377FA29ECD90661255

Key	Value
MD5	06A7C768308BB5F4EA3A2BDA9960946D
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python38-emcee
PackageRelease	7.10
PackageVersion	3.1.1
SHA-1	0B83C84B987968ADFE53508D88E3B920807BCD9D
SHA-256	1BA7BFDF31C878A77335B985A224122CE37151EE16B3443B5203628B3ED7D07A

Key	Value
MD5	387069683E9154BE807FBC9446C67658
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python39-emcee
PackageRelease	1.1
PackageVersion	3.1.0
SHA-1	12298657739E619DF5C7E0337ACFD9FAF0093138
SHA-256	6DA021854063D785C086F782084E011877FDDAF69968E5C004626E3F4901E778

Key	Value
MD5	DFA9691B2D38D4CF783A0476876D5D8B
PackageArch	noarch
PackageDescription	Emcee 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
PackageMaintainer	https://bugs.opensuse.org
PackageName	python38-emcee
PackageRelease	1.2
PackageVersion	3.1.1
SHA-1	1ADB56B575FD15742109771FE6619FC0510B4F46
SHA-256	E12BAA04B4EF0A16620DF2BE87562912304FE4203EC67141C483C73C6C210476

Key	Value
MD5	AD964B64831F212D3B01875019EB9C81
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python39-emcee
PackageRelease	7.12
PackageVersion	3.1.1
SHA-1	254A60D826B0452AA9EE299DB3156CED3849758A
SHA-256	8F9A0B28EC754E28D63E893C940B4FB63EC39C2EBE6B1711281EDC71E87B1E98

Key	Value
MD5	3E4B6178816B41DB46D3EA46EF2AB1D5
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python39-emcee
PackageRelease	7.11
PackageVersion	3.1.1
SHA-1	2A680AEC6E675EC28CB68BBAE4F855C3B6F808FC
SHA-256	B138C47B80E6F83A1E4B88D01FF296F7FB16AF563814919589B56418177746CB

Key	Value
MD5	73C4AF9D87621DE9B5DF374835C9388A
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python310-emcee
PackageRelease	7.11
PackageVersion	3.1.1
SHA-1	3495D3E0FC0B9B1B577BBCF4FE72F9BF9F3F917D
SHA-256	4A5B2A9709D3106DE0D352AC95684BE1D62CD3D981CFD141062CA8AC6F00B16A

Key	Value
MD5	8BB9C19AF1E7D1F7C6AC1A638A2F90A5
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python3-emcee
PackageRelease	2.4
PackageVersion	3.1.1
SHA-1	39298F9D84B6D35467F3052EAEF97DFD2FA1DFA7
SHA-256	72A7E780B39DD6C272F7AEF220307E831831E259A384C7DB6E16B07ACB7F1899

Key	Value
MD5	52D86774823AF30231A30C358FC7ACB3
PackageArch	noarch
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.
PackageMaintainer	umeabot <umeabot>
PackageName	python3-emcee
PackageRelease	3.mga9
PackageVersion	3.1.1
SHA-1	3E2BCA13E99F20D16BF5D81B414DD0950B0D02BA
SHA-256	D3EF1E9BC2A171DFF8F47848917991DFD1C999CDEA9F9EE8CA1B948159786487

Key	Value
MD5	681A3BDA4BFF5577C9D379B19382BF4D
PackageArch	noarch
PackageDescription	Emcee 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
PackageName	python310-emcee
PackageRelease	7.10
PackageVersion	3.1.1
SHA-1	46DAF951D75EA3F5F6958A57E762BF022B22E836
SHA-256	8B4588B16129AAAD4D325FD282CE17B754EC3F9168C769757338422B09778CE7