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r-cran-metafor package

r-cran-metafor 3.0-2-1 source package in Ubuntu

Changelog

r-cran-metafor (3.0-2-1) unstable; urgency=medium

  * New upstream version
  * Standards-Version: 4.6.0 (routine-update)
  * debhelper-compat 13 (routine-update)
  * dh-update-R to update Build-Depends (3) (routine-update)

 -- Andreas Tille <email address hidden>  Mon, 06 Sep 2021 14:28:46 +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
Jammy release universe misc

Builds

Jammy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-metafor_3.0-2-1.dsc 2.2 KiB 5da797cbaac18d651173283c1e59f68c7cdd4d491fbf7c7eab862973edc467a7
r-cran-metafor_3.0-2.orig.tar.gz 3.4 MiB 02df435197b225da736103edf73d19253a542bc31fc0b99610c02daec434138a
r-cran-metafor_3.0-2-1.debian.tar.xz 10.7 KiB a50da7c4a6c84894de4d92bdc5d8dcb1234813d3407bd042324df40d761b9775

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.