Ubuntu
r-cran-metafor package

r-cran-metafor 4.2-0-1 source package in Ubuntu

Changelog

r-cran-metafor (4.2-0-1) unstable; urgency=medium

  * New upstream version
  * Standards-Version: 4.6.2 (routine-update)
  * dh-update-R to update Build-Depends (routine-update)
  * Sync Test-Depends with Suggests

 -- Andreas Tille <email address hidden>  Mon, 26 Jun 2023 09:05:26 +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
Mantic release universe misc

Builds

Mantic: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-metafor_4.2-0-1.dsc 2.5 KiB bc6b0a74931ccf64e324b991b1b5260f2cb6754c1212d6818ff86f2a8287e31e
r-cran-metafor_4.2-0.orig.tar.gz 4.5 MiB f1bb423b4f568c1af9b2c21bf368308b054bcb83d39a9d67273e43596be0a766
r-cran-metafor_4.2-0-1.debian.tar.xz 11.0 KiB 1b43059addcd9b1e461538a9a71ccab7ae872dc291e690ca7a6ce4f27e93c407

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.