Meta-Analytic Selection Models for Dependent Effect Sizes

Documentation for package ‘metaselection’ version 0.3.0

beta_fun	Censor meta-analytic dataset based on the univariate beta-density model
define_priors	Define prior penalty functions for selection model parameters
interleaved_learning	Interleaved Learning Meta-Analysis
n_ES_empirical	Simulate empirical distribution of sample size and number of effect sizes
n_ES_param	Simulate empirical distribution of sample size and number of effect sizes
practice_facilitation	Practice Facilitation Meta-Analysis
print.selmodel	Print results from a 'selmodel' object
p_area	Calculate area under the selection weight function from a 'selmodel' object
r_meta	Generate meta-analytic data
selection_model	Estimate step or beta selection model
selection_plot	Plot the selection weights implied by an estimated selection model.
selection_plot.boot.selmodel	Plot the selection weights implied by an estimated selection model.
selection_plot.selmodel	Plot the selection weights implied by an estimated selection model.
selection_wts	Calculate model-implied weights for specified p-values.
selection_wts.beta.selmodel	Calculate model-implied weights for specified p-values.
selection_wts.step.selmodel	Calculate model-implied weights for specified p-values.
self_control	Self-Control Training Meta-Analysis
step_count_fun	Censor meta-analytic dataset based on a multivariate step-function model
step_fun	Censor meta-analytic dataset based on a univariate step-function model
summary.selmodel	Summarize results from a 'selmodel' object
wwc_es	What Works Clearinghouse sample size and effect size distribution data