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 | Published | Component | Section | |
---|---|---|---|---|
Mantic | release | universe | misc |
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
- diff from 3.8-1-1 to 4.2-0-1 (220.2 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.