Parents (Total: 17)

The searched file hash is included in 17 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	375C60E11D97D8CB8BF61032A2BEFF6A
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	lp152.2.6
PackageVersion	2.15.1
SHA-1	02A686E3FFA30509C7C5088BC21AA5B2B1C8B656
SHA-256	2AB0D5EE58D7D6C0C885AA4A6FE0751AE33B5DB98028CA44E55B479D04AD26BF

Key	Value
MD5	64843E9F943287E02554A7A041208BBC
PackageArch	armv7hl
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.29
PackageVersion	2.15.1
SHA-1	04A5D34A6594A62300818A4D7F3063117829F5E5
SHA-256	D4F1383DF3A0AB9A41D6D8EF700A6D1A76BB788EFEE17B72C219328304BADEAE

Key	Value
MD5	55CEF3040BB4E6587F6FDB54B3D61908
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.15
PackageVersion	2.15.1
SHA-1	1397DA39611B19EA6EB03048B84CC60B7F18158D
SHA-256	952589FE5D29AB560A461FF5D823F42022CA94EE54FEF6586CD29500B86068DC

Key	Value
MD5	86418BF5A63DBDE56810F5760C162BFC
PackageArch	i586
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.31
PackageVersion	2.15.1
SHA-1	1FF3EB703FF5F059010D801B82C08FBCC20268F4
SHA-256	B42334EA61BD9C45C383A7046AC9A34A4CB094AC0D19506301041EDB6B08675F

Key	Value
MD5	51BEBBEF62A6EC58665B3CF8251DDDA0
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	2.24
PackageVersion	2.15.1
SHA-1	2F410197AE466C911CEF2387901C9ACBD4BE4A49
SHA-256	277315D4085F9F6BEDB5E96A6B183D352D4DCA52F0ECF23FB328A91554B29AAD

Key	Value
MD5	E28A09FBBF0B70292CDFBC8C425B365F
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	lp151.1.12
PackageVersion	2.15.1
SHA-1	3866BDFFBD03C0DE6496CC97A4632638D2B5EF72
SHA-256	3198FB694FA693F89782B68883F6805D2367C145C44EF7FFBC004DBC571145A1

Key	Value
MD5	262A780CC035222BAA24361042771FED
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	lp153.1.14
PackageVersion	2.15.1
SHA-1	5DDD47D6BA887CA7AB2ADC2B167E771C88650597
SHA-256	36D7B918F93244A8360F7F62C9B2EC8B10BF90F4C2F788A570ADA9791B1F8F18

Key	Value
MD5	4702EDED0CACDAD0B9C77117ED13C423
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.6
PackageVersion	2.15.1
SHA-1	5F1F4961C6C269CF85AD0C9163567C35301A006D
SHA-256	7A57729829B4FBB4234E1F9F8D954D9E05B50169B5EE9A38E5B142DD7ADFED10

Key	Value
MD5	B685B913CD2C18A916FB45308DCC68A5
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.31
PackageVersion	2.15.1
SHA-1	668F26064F3AF757874499A0B4BA51E5B39E1224
SHA-256	1A58DF996E307D77188BAE983899A6D320D6AA1CC3B3A50F63E80200F359EAE3

Key	Value
MD5	C09E4A7A977553F4E3F18E51F611D43F
PackageArch	x86_64
PackageDescription	A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
PackageName	R-LearnBayes
PackageRelease	1.12
PackageVersion	2.15.1
SHA-1	66D1F0FED88EA321B40DABDBAC027B824E8785B5
SHA-256	814D624DE8E0EC279DC329C061F14312132DAD4EB84F05BFB8B9D5EC75099399