Estimation of Weight Functions in Meta Analysis

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Documentation for package ‘selectMeta’ version 1.0.9

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selectMeta-package Estimation of Weight Functions in Meta Analysis
DearBegg Compute the nonparametric weight function from Dear and Begg (1992)
DearBeggLoglik Compute the nonparametric weight function from Dear and Begg (1992)
DearBeggMonotone Compute the nonparametric weight function from Dear and Begg (1992)
DearBeggMonotoneCItheta Compute an approximate profile likelihood ratio confidence interval for effect estimate
DearBeggMonotonePvalSelection Compute simulation-based p-value to assess null hypothesis of no selection
DearBeggProfileLL Compute an approximate profile likelihood ratio confidence interval for effect estimate
DearBeggToMinimize Compute the nonparametric weight function from Dear and Begg (1992)
DearBeggToMinimizeProfile Compute an approximate profile likelihood ratio confidence interval for effect estimate
dPval Functions for the distribution of p-values
education Dataset open vs. traditional education on creativity
effectBias Compute bias for each effect size based on estimated weight function
Hij Compute the nonparametric weight function from Dear and Begg (1992)
IyenGreen Compute MLE and weight functions of Iyengar and Greenhouse (1988)
IyenGreenLoglikT Compute MLE and weight functions of Iyengar and Greenhouse (1988)
IyenGreenMLE Compute MLE and weight functions of Iyengar and Greenhouse (1988)
IyenGreenWeight Compute MLE and weight functions of Iyengar and Greenhouse (1988)
normalizeT Compute MLE and weight functions of Iyengar and Greenhouse (1988)
passive_smoking Dataset on the effect of environmental tobacco smoke
pPool Pool p-values in pairs
pPval Functions for the distribution of p-values
Pval Functions for the distribution of p-values
qPval Functions for the distribution of p-values
rPval Functions for the distribution of p-values
selectMeta Estimation of Weight Functions in Meta Analysis
weightLine Function to plot estimated weight functions