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author | Ricardo Wurmus <rekado@elephly.net> | 2018-10-25 05:16:44 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2018-10-25 05:17:42 +0200 |
commit | aa4bde0b3a8e012124fabdfd0c74a76abd24597d (patch) | |
tree | 3a290aa1a8a45ea47558327ec18804c1bc4d4f9c | |
parent | 3b8a3f554811f675bc68d997476dabd8bf82ae1f (diff) | |
download | guix-aa4bde0b3a8e012124fabdfd0c74a76abd24597d.tar.gz |
gnu: Add r-sjstats.
* gnu/packages/cran.scm (r-sjstats): New variable.
-rw-r--r-- | gnu/packages/cran.scm | 46 |
1 files changed, 46 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 77eb389bdd..70cba10132 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -6483,3 +6483,49 @@ from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.") (license license:gpl2))) + +(define-public r-sjstats + (package + (name "r-sjstats") + (version "0.17.1") + (source + (origin + (method url-fetch) + (uri (cran-uri "sjstats" version)) + (sha256 + (base32 + "0s9b0xr8gmcm0kh48jkwk8ns0lqknd8kpys4f7czf0xwzmcx2n1z")))) + (build-system r-build-system) + (propagated-inputs + `(("r-bayesplot" ,r-bayesplot) + ("r-broom" ,r-broom) + ("r-coin" ,r-coin) + ("r-crayon" ,r-crayon) + ("r-dplyr" ,r-dplyr) + ("r-emmeans" ,r-emmeans) + ("r-glmmtmb" ,r-glmmtmb) + ("r-lme4" ,r-lme4) + ("r-magrittr" ,r-magrittr) + ("r-mass" ,r-mass) + ("r-matrix" ,r-matrix) + ("r-modelr" ,r-modelr) + ("r-nlme" ,r-nlme) + ("r-purrr" ,r-purrr) + ("r-pwr" ,r-pwr) + ("r-rlang" ,r-rlang) + ("r-sjlabelled" ,r-sjlabelled) + ("r-sjmisc" ,r-sjmisc) + ("r-tidyr" ,r-tidyr))) + (home-page "https://github.com/strengejacke/sjstats") + (synopsis "Functions for common statistical computations") + (description + "This package provides a collection of convenient functions for common +statistical computations, which are not directly provided by R's @code{base} +or @code{stats} packages. This package aims at providing, first, shortcuts +for statistical measures, which otherwise could only be calculated with +additional effort. Second, these shortcut functions are generic, and can be +applied not only to vectors, but also to other objects as well. The focus of +most functions lies on summary statistics or fit measures for regression +models, including generalized linear models, mixed effects models and Bayesian +models.") + (license license:gpl3))) |