r-cran-metafor 2.4-0-2build1 source package in Ubuntu

Changelog

r-cran-metafor (2.4-0-2build1) groovy; urgency=medium

  * No-change rebuild against r-api-4.0

 -- Graham Inggs <email address hidden>  Sat, 30 May 2020 16:39:52 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Groovy
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Impish release universe misc
Hirsute release universe misc
Groovy release universe misc

Builds

Groovy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-metafor_2.4-0.orig.tar.gz 1.3 MiB b64a678b762e91f1e0a6360b15e79fe19159b243c9f40ad9cc0be833bb4ba9ac
r-cran-metafor_2.4-0-2build1.debian.tar.xz 10.7 KiB 1d3783c691d112f7c3f4d49b4aa8019177b931b9d67539dda7bbc9ceb64280d8
r-cran-metafor_2.4-0-2build1.dsc 2.2 KiB 2090d988dc2bf535542e5b136952df8c1c641c041914467aae603815470cfdde

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Binary packages built by this source

r-cran-metafor: Meta-Analysis Package for R

 A comprehensive collection of functions for conducting meta-analyses in
 R. The package includes functions to calculate various effect sizes or
 outcome measures, fit fixed-, random-, and mixed-effects models to such
 data, carry out moderator and meta-regression analyses, and create
 various types of meta-analytical plots (e.g., forest, funnel, radial,
 L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-
 time data, the package also provides functions that implement
 specialized methods, including the Mantel-Haenszel method, Peto's
 method, and a variety of suitable generalized linear (mixed-effects)
 models (i.e., mixed-effects logistic and Poisson regression models).
 Finally, the package provides functionality for fitting meta-analytic
 multivariate/multilevel models that account for non-independent sampling
 errors and/or true effects (e.g., due to the inclusion of multiple
 treatment studies, multiple endpoints, or other forms of clustering).
 Network meta-analyses and meta-analyses accounting for known correlation
 structures (e.g., due to phylogenetic relatedness) can also be
 conducted.