Parents (Total: 31)

The searched file hash is included in 31 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	49B25D4F0A5F71B0FD3A7A4793FBD9E3
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	Fedora Project
PackageName	python-emcee
PackageRelease	1.fc21
PackageVersion	2.1.0
SHA-1	010B5EC003E5C8D7150C20C22E1E6D3D56C3F41C
SHA-256	F2357D3FF6FA4826CB51F69F2802A27963241F11F6ED5236F0659BEC88914A5D

Key	Value
FileSize	20878
MD5	DB5C74813F73B5D3FB76B4D17EA27E8E
PackageDescription	Affine-invariant ensemble MCMC sampling for Python 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. . This is the Python 2 package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-emcee
PackageSection	python
PackageVersion	2.1.0-5
SHA-1	07A6AD42D90C8AE4C36D89D7AFC8772F00F4D050
SHA-256	6BE3C88F3F83E4B872ABE37ADEFA8D939C882C6619C0246AAA8F9DBF627C3AF1

Key	Value
FileName	16141
FileSize	22198
MD5	3F38654D22AEC622EA797B3B8D242845
PackageDescription	Affine-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. . This is the Python 3 package.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-emcee
PackageSection	python
PackageVersion	2.2.1-1
RDS:package_id	182052
SHA-1	09D1E0A13584F01DED0DF4A3753B4A2FFB2529F3
SHA-256	BB393D77536CFBC3AC14A446434381E726960D5D5210428C4906ABEEA9B29588
insert-timestamp	1679408376.223349
source	RDS.db

Key	Value
MD5	6A9B21A7C9175BE1E4231D1A851A836E
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	Fedora Project
PackageName	python-emcee
PackageRelease	1.fc22
PackageVersion	2.1.0
SHA-1	1BF38990CEAF48A988DD1E292C38EB9FA0889A71
SHA-256	990EBB9C8E2CD15EE1F6AE99845E34F1ED2A3AC3DD5455685B6A0BCE6D288F97

Key	Value
MD5	E2FCA6600A68F8545ADC4920CF329FB4
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	Fedora Project
PackageName	python3-emcee
PackageRelease	2.fc23
PackageVersion	2.1.0
SHA-1	1D5B1CCBAEBA8870DA547BCED2A04EA7DB2A89A8
SHA-256	D9F8F04BBB1CFC302AC52813A3E3E69DF3625A9C19ED9C683056A7B5B7EA3E1F

Key	Value
MD5	3ACD9715ED7E7CE3BB1509E75300D628
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	Fedora Project
PackageName	python3-emcee
PackageRelease	1.fc21
PackageVersion	2.1.0
SHA-1	24AF5B886CEC89701F439AF9CE2259F2C93DBD42
SHA-256	A7C9F0C2CF1FCE2F236B62DE671BA13C43A5E83BDF4C140EC17D4E01A9B0F2E5

Key	Value
MD5	E7D66E5042562A527DBFB3BA55D9FDD0
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	Fedora Project
PackageName	python3-emcee
PackageRelease	1.fc21
PackageVersion	2.1.0
SHA-1	2C012AB637250C2E7C347B70BEBF9104BF3F669A
SHA-256	8996637B082D2081A45B9AB860AA23C8A5D83ABEFDE6432B57FC5EFF4FDBA54A

Key	Value
FileSize	23276
MD5	466EE0312B08EA363A6A7430A14E6677
PackageDescription	Affine-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. . This is the Python 3 package.
PackageMaintainer	Debian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org>
PackageName	python3-emcee
PackageSection	python
PackageVersion	2.2.1-1
SHA-1	39F35B4B300A54CFFBC89E8494E16D88BAC838F1
SHA-256	E94D4A4AD2D80BB1B8E4E8125F1D3D5A23CBF54B9F055552748747E2DF863346

Key	Value
MD5	5B73D6962F7B23D3E4C5238762F89282
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	Fedora Project
PackageName	python-emcee
PackageRelease	2.fc23
PackageVersion	2.1.0
SHA-1	3B1AAF0E78105EB3063DDD0BB27F37473628F3C7
SHA-256	2899BCC2A1EA99CB5B10CA360EA292E79823A85A451AEDA16831F09D6BDA6DC0

Key	Value
MD5	F1332DF4F3F5DA31266EBE9680F12803
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	Fedora Project
PackageName	python3-emcee
PackageRelease	2.fc23
PackageVersion	2.1.0
SHA-1	435C417632547479149EFA2AAC7E1AB37BEAAC5C
SHA-256	ED059078AD2E17617E89A452240974395C93C9C1145E37D2A30E6B6E173920FE