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gnu: r-abn: Update to 2.5-0.

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* gnu/packages/cran.scm (r-abn): Move from here...
* gnu/packages/bioconductor.scm (r-abn): ...to here; update to 2.5-0.
[propagated-inputs]: Add r-rgraphviz.
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Ricardo Wurmus 2021-05-04 07:09:50 +02:00
parent b9fb13b284
commit aa9a94bba9
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2 changed files with 38 additions and 35 deletions
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38
gnu/packages/bioconductor.scm
View file

@ -10996,6 +10996,44 @@ optionally include the physical locations or genetic map distances of each SNP
on the plot.")
(license license:gpl3)))
;; This is a CRAN package, but it depends on r-rgraphviz, which is a
;; Bioconductor package.
(define-public r-abn
(package
(name "r-abn")
(version "2.5-0")
(source
(origin
(method url-fetch)
(uri (cran-uri "abn" version))
(sha256
(base32
"1fqmhw0mhdl6az1gpg0byvx5snhz1pl3fqikhyfjcjrc9xbsq8yw"))))
(build-system r-build-system)
(inputs
`(("gsl" ,gsl)))
(propagated-inputs
`(("r-lme4" ,r-lme4)
("r-nnet" ,r-nnet)
("r-rcpp" ,r-rcpp)
("r-rcpparmadillo" ,r-rcpparmadillo)
("r-rgraphviz" ,r-rgraphviz)
("r-rjags" ,r-rjags)))
(home-page "https://r-bayesian-networks.org/")
(synopsis "Modelling multivariate data with additive bayesian networks")
(description
"Bayesian network analysis is a form of probabilistic graphical models
which derives from empirical data a directed acyclic graph, DAG, describing
the dependency structure between random variables. An additive Bayesian
network model consists of a form of a DAG where each node comprises a
@dfn{generalized linear model} (GLM). Additive Bayesian network models are
equivalent to Bayesian multivariate regression using graphical modelling, they
generalises the usual multivariable regression, GLM, to multiple dependent
variables. This package provides routines to help determine optimal Bayesian
network models for a given data set, where these models are used to identify
statistical dependencies in messy, complex data.")
(license license:gpl2+)))
(define-public r-pathview
(package
(name "r-pathview")

35
gnu/packages/cran.scm
View file

@ -8037,41 +8037,6 @@ mutual information, and chi-squared statistic of independence. In addition
there are functions for discretizing continuous random variables.")
(license license:gpl3+)))
(define-public r-abn
(package
(name "r-abn")
(version "2.3-0")
(source
(origin
(method url-fetch)
(uri (cran-uri "abn" version))
(sha256
(base32
"17vdrqm6qp5aijg00ah2imj3pqr6cl5r43hgg8dijbrbhznarci6"))))
(build-system r-build-system)
(inputs
`(("gsl" ,gsl)))
(propagated-inputs
`(("r-lme4" ,r-lme4)
("r-nnet" ,r-nnet)
("r-rcpp" ,r-rcpp)
("r-rcpparmadillo" ,r-rcpparmadillo)
("r-rjags" ,r-rjags)))
(home-page "https://r-bayesian-networks.org/")
(synopsis "Modelling multivariate data with additive bayesian networks")
(description
"Bayesian network analysis is a form of probabilistic graphical models
which derives from empirical data a directed acyclic graph, DAG, describing
the dependency structure between random variables. An additive Bayesian
network model consists of a form of a DAG where each node comprises a
@dfn{generalized linear model} (GLM). Additive Bayesian network models are
equivalent to Bayesian multivariate regression using graphical modelling, they
generalises the usual multivariable regression, GLM, to multiple dependent
variables. This package provides routines to help determine optimal Bayesian
network models for a given data set, where these models are used to identify
statistical dependencies in messy, complex data.")
(license license:gpl2+)))
(define-public r-acd
(package
(name "r-acd")