Package: pema 0.1.3

pema: Penalized Meta-Analysis

Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero.

Authors:Caspar J van Lissa [aut, cre], Sara J van Erp [aut]

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pema/json (API)

# Install 'pema' in R:

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

Bug tracker:https://github.com/cjvanlissa/pema/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

bonapersona - Data from 'The behavioral phenotype of early life adversity'
curry - Data from 'Happy to Help?'

On CRAN:

8 exports 1.13 score 81 dependencies 20 scripts 1.6k downloads

Last updated 1 years agofrom:3f7fb4a009. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 27 2024
R-4.5-win-x86_64	NOTE	Aug 27 2024
R-4.5-linux-x86_64	NOTE	Aug 27 2024
R-4.4-win-x86_64	NOTE	Aug 27 2024
R-4.4-mac-x86_64	NOTE	Aug 27 2024
R-4.4-mac-aarch64	NOTE	Jul 28 2024
R-4.3-win-x86_64	NOTE	Aug 27 2024
R-4.3-mac-x86_64	NOTE	Aug 27 2024
R-4.3-mac-aarch64	NOTE	Aug 27 2024

Exports:as.stan brma I2 maxap plot_sensitivity sample_prior shiny_prior simulate_smd

Dependencies:abind backports base64enc BH bslib cachem callr checkmate cli colorspace commonmark crayon desc digest distributional fansi farver fastmap fontawesome fs generics ggplot2 glue gridExtra gtable htmltools httpuv inline isoband jquerylib jsonlite labeling later lattice lifecycle loo magrittr MASS Matrix MatrixModels matrixStats memoise mgcv mime mnormt munsell nlme numDeriv pillar pkgbuild pkgconfig posterior processx promises ps quantreg QuickJSR R6 rappdirs RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan rstantools sass scales shiny sn sourcetools SparseM StanHeaders survival tensorA tibble utf8 vctrs viridisLite withr xtable

Conducting a Bayesian Regularized Meta-analysis

Rendered fromusing-brma.Rmdusingknitr::rmarkdownon Aug 27 2024.

Last update: 2022-07-16
Started: 2022-04-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
pema: Conduct penalized meta-regression.	pema-package pema
Convert an object to stanfit	as.stan
Data from 'The behavioral phenotype of early life adversity'	bonapersona
Conduct Bayesian Regularized Meta-Analysis	brma brma.default brma.formula
Data from 'Happy to Help?'	curry
Compute I2	I2
Maximum a posteriori parameter estimate	maxap
Plot posterior distributions for BRMA models	plot_sensitivity
Sample from the Prior Distribution	sample_prior
Interactively Sample from the Prior Distribution	shiny_prior
Simulates a meta-analytic dataset	simulate_smd