encode (>= 0.3.6), lattice, magrittr, dplyr (>= 0.7.1), tidyr,
rlang, grid, gridExtra, gtable, ggplot2, scales

Creates CrossHairs Plots for Meta-Analyses
c(
person("Mehmet", "Gultas", , "mehmetgultas@gmail.com", c("aut", "cre")),
person("Michael T.", "Brannick", , "mbrannick@usf.edu", "aut")
)
Mehmet Gultas [aut, cre],
Michael T. Brannick [aut]
Creates crosshairs plots to summarize and analyse
meta-analysis results. In due time this package will contain code
that will create other kind of meta-analysis graphs.

Performs meta-analysis using standard and robust methods with confidence intervals from profile likelihood.

c(person("Ken","Beath",email="ken.beath@mq.edu.au", role=c("aut","cre")),
person("Ben","Bolker",email="bolker@mcmaster.ca",role="aut"),
person("R Development Core Team",role="aut"))
Ken Beath [aut, cre],
Ben Bolker [aut],
R Development Core Team [aut]
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
bbmle, metafor, boot, methods, numDeriv, MASS, graphics,
stats, fastGHQuad, lme4

Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals from the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 doi:10.1002/sim.2897 or Baker and Jackson, 2008 doi:10.1007/s10729-007-9041-8) or mixtures of normals (Beath, 2014 doi:10.1002/jrsm.1114).

boot, methods, numDeriv, lme4, MASS, graphics, stats

Ken Beath [aut, cre], Ben Bolker [aut], R Development Core Team [aut]
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 doi:10.1002/sim.2897 or Baker and Jackson, 2008 doi:10.1007/s10729-007-9041-8) or mixtures of normals (Beath, 2014 doi:10.1002/jrsm.1114).