Graphical User Interface for Conducting Meta-Analyses in R
This is an R-Commander plug-in for meta-analysis. This
package enables the user to conduct a meta-analysis in a
menu-driven, graphical user interface environment (e.g., SPSS).
It uses recommended procedures as described in The Handbook of
Research Synthesis and Meta-Analysis (Cooper, Hedges, &
Valentine, 2009).

Easy to use interface for conducting meta-analysis in R. This
package is an Rcmdr-plugin, which allows the user to conduct analyses in a
menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
uses recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
knitr, rmarkdown, compute.es, ggplot2, gridExtra, scales

Rcmdr Plug-in for Meta-Analysis with Correlations (MAc)
This is an R-Commander plug-in for the MAc package
(Meta-Analysis for Correlations). This package enables the user
to conduct a meta-analysis in a menu-driven, graphical user
interface environment (e.g., SPSS), while having the full
statistical capabilities of R and the MAc package. The MAc
package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of
the unique features of the MAc package is in its integration of
user-friendly functions to complete all statistical steps
involved in a meta-analysis with correlations. It uses
recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine,
2009).

R (>= 2.10.1), Rcmdr, MAc, plyr, ggplot2

This is an R-Commander plug-in for the MAc package
(Meta-Analysis with Correlations). This package enables the
user to conduct a meta-analysis in a menu-driven, graphical
user interface environment (e.g., SPSS), while having the full
statistical capabilities of R and the MAc package. The MAc
package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of
the unique features of the MAc package is in its integration of
user-friendly functions to complete all statistical steps
involved in a meta-analysis with correlations. It uses
recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine,
2009).