r-cran-metafor 4.4-0-1 source package in Ubuntu
Changelog
r-cran-metafor (4.4-0-1) unstable; urgency=medium * New upstream version -- Andreas Tille <email address hidden> Sat, 14 Oct 2023 14:23:53 +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 | Published | Component | Section | |
|---|---|---|---|---|
| Noble | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-cran-metafor_4.4-0-1.dsc | 2.5 KiB | b1fb6e7e198c446689ee2e5d7c4da5546e0b9e503bab695a489947e3d92828ed |
| r-cran-metafor_4.4-0.orig.tar.gz | 4.5 MiB | 62aca0c70b44205e885cf55f6cfb56c37efff74bdef79dbabd727d629d3087d4 |
| r-cran-metafor_4.4-0-1.debian.tar.xz | 11.0 KiB | b4d842ee2a22ce513418a5c5d898d51f021d600be1648c7fdf23160b84dfe7b2 |
Available diffs
- diff from 4.2-0-1 to 4.4-0-1 (310.1 KiB)
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.
