Package: metaselection 0.3.0.9999

metaselection: Meta-Analytic Selection Models for Dependent Effect Sizes

Fits a flexible class of p-value selection models for meta-analysis and meta-regression models, providing standard errors and confidence intervals based on either cluster-robust variance estimators (i.e., sandwich estimators) or cluster-level bootstrapping to handle dependent effect size estimates, as described in Pustejovsky, Citkowicz, and Joshi (2025) <doi:10.31222/osf.io/qg5x6_v1> and Citkowicz, Pustejovsky, and Joshi (2026) <doi:10.31222/osf.io/wjpxk_v1>. Supported models include generalizations of the step-function selection model as proposed by Vevea and Hedges (1995) <doi:10.1007/BF02294384> and the beta-function selection model as proposed by Citkowicz and Vevea (2017) <doi:10.1037/met0000119>.

Authors:James E. Pustejovsky [aut, cre], Megha Joshi [aut], Martyna Citkowicz [aut]

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metaselection/json (API)

# Install 'metaselection' in R:

install.packages('metaselection', repos = c('https://jepusto.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jepusto/metaselection/issues

Datasets:

interleaved_learning - Interleaved Learning Meta-Analysis
practice_facilitation - Practice Facilitation Meta-Analysis
self_control - Self-Control Training Meta-Analysis
wwc_es - What Works Clearinghouse sample size and effect size distribution data

On CRAN:

meta-analysis

6.66 score 5 stars 36 scripts 416 downloads 11 exports 50 dependencies

Last updated from:509422668d. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	219
source / vignettes	OK	325
linux-release	OK	209
macos-release	OK	184
macos-oldrel	OK	179
windows-devel	OK	181
windows-release	OK	159
windows-oldrel	OK	162
wasm-release	OK	148

Exports:beta_fun define_priors n_ES_empirical n_ES_param p_area r_meta selection_model selection_plot selection_wts step_count_fun step_fun

Dependencies:cli codetools cpp11 digest dplyr farver Formula furrr future future.apply generics ggplot2 globals glue gtable isoband labeling lifecycle listenv magrittr MASS mvtnorm nleqslv nloptr numDeriv optimx parallelly pillar pkgconfig pracma progressr purrr R6 rbibutils RColorBrewer Rdpack rlang rstudioapi S7 scales simhelpers stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

P-Value Selection Models for Meta-Analysis with Dependent Effects

Rendered fromselection-models.Rmdusingknitr::rmarkdown

Last update: 2026-08-18
Started: 2024-10-09

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

Package: metaselection 0.3.0.9999

metaselection: Meta-Analytic Selection Models for Dependent Effect Sizes

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