Ubuntu
r-cran-metafor package

r-cran-metafor 3.8-1-1 source package in Ubuntu

Changelog

r-cran-metafor (3.8-1-1) unstable; urgency=medium

  * New upstream version

 -- Andreas Tille <email address hidden>  Thu, 15 Sep 2022 15:41:08 +0200

Upload details

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

See full publishing history Publishing

Series Pocket Published Component Section
Lunar release universe misc

Builds

Lunar: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-metafor_3.8-1-1.dsc 2.3 KiB 6a5797e61fb0ef7f19508408c941426b5984f839bd744af9e707f3fa241d97b1
r-cran-metafor_3.8-1.orig.tar.gz 3.3 MiB d694577f954144d8a5eeab6521fe1c87e68ddf9ecfd7ccc915d01533371b0514
r-cran-metafor_3.8-1-1.debian.tar.xz 10.8 KiB ac6a584f79d64264b968e5e7af4fded3dcdb0413aab2bef402375bec026f5d36

Available diffs

No changes file available.

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.