Ubuntu
r-cran-metafor package

r-cran-metafor 4.6-0-1 source package in Ubuntu

Changelog

r-cran-metafor (4.6-0-1) unstable; urgency=medium

  * Team upload.
  * New upstream version
  * Standards-Version: 4.7.0 (routine-update)

 -- Charles Plessy <email address hidden>  Thu, 11 Jul 2024 14:54:03 +0900

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
Oracular release universe misc

Builds

Oracular: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-metafor_4.6-0-1.dsc 2.5 KiB 0ae1cff2c28a0d27a433016e84b1917d5c5ee3ad92867967e743a8ee4285d6d9
r-cran-metafor_4.6-0.orig.tar.gz 4.5 MiB 07344cc3bd87b8bd25ef998e9a6ce322ae8e448ef5af06ec3e79631724e18666
r-cran-metafor_4.6-0-1.debian.tar.xz 11.0 KiB 7c409d32464c96c2eed791a2f9889ebca06f23c994428a9dbe882a61b8df0b0b

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.