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author | Ricardo Wurmus <rekado@elephly.net> | 2021-05-04 07:09:50 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2021-05-04 07:27:43 +0200 |
commit | aa9a94bba9a5705c6a4bfb19268dc616b7f4f7d3 (patch) | |
tree | e7aa63d71acf922f52c95348fe75d0cecce98864 /gnu/packages/cran.scm | |
parent | b9fb13b28437a254683273094f189396a6e1421d (diff) | |
download | guix-patches-aa9a94bba9a5705c6a4bfb19268dc616b7f4f7d3.tar guix-patches-aa9a94bba9a5705c6a4bfb19268dc616b7f4f7d3.tar.gz |
gnu: r-abn: Update to 2.5-0.
* 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.
Diffstat (limited to 'gnu/packages/cran.scm')
-rw-r--r-- | gnu/packages/cran.scm | 35 |
1 files changed, 0 insertions, 35 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 35c35e6716..d5b71d65d3 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -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") |