bbmle, metafor, boot, methods, numDeriv, lme4, MASS, graphics,
stats, fastGHQuad

bbmle, metafor, boot, methods, numDeriv, MASS, graphics,
stats, fastGHQuad, lme4, Rfast, parallel, doParallel, foreach,
doRNG

c(person("Ken","Beath",email="ken@kjbeath.com.au", role=c("aut","cre")),
person("Ben","Bolker",email="bolker@mcmaster.ca",role="aut"),
person("R Development Core Team",role="aut"))

Provides an interface to 'MetaPost' (Hobby, 1998)
<http://www.tug.org/docs/metapost/mpman.pdf>.
There are functions to generate an R description of a 'MetaPost'
curve, functions to generate 'MetaPost' code from an R description,
functions to process 'MetaPost' code, and functions to read
solved 'MetaPost' paths back into R.

person("Jason", "Griffin", email = "jasongriffin138@gmail.com", role = c("aut", "cre"))
A simple and effective tool for computing meta-analytic statistical power for
main effects, tests of homogeneity, and categorical moderator models. All equations are
described in Pigott (2012) <doi:10.1007/978-1-4614-2278-5>,
Hedges & Pigott (2004) <doi:10.1037/1082-989X.9.4.426>, and
Borenstein, Hedges, Higgins, & Rothstein (2009) <doi:10.1002/9780470743386>.

A simple and effective tool for computing and visualizing statistical power for meta-analysis,
including power analysis of main effects, test of homogeneity, subgroup analysis, and categorical moderator analysis.

A simple and effective tool for computing and visualizing statistical power for meta-analysis,
including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>,
test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis,
and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.

The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new methods such as weighted Fisher’s method and ordmeta we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method and ordmeta are useful for detecting partially associated features. Thus, the users can choose the function based on their purpose.