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spima: Simulated Pseudo-Individual Data Meta-Analysis with ABC-SMC

Meta-analysis via Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) by simulating pseudo-individual data from published group-level summary statistics. Handles binary, continuous, and generic effect-size outcomes within a one-stage mixed-model framework. Supports subgroup analysis.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: lme4, parallel, stats, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggplot2, testthat, knitr, rmarkdown
Published: 2026-05-28
DOI: 10.32614/CRAN.package.spima
Author: Yu Haichuan [aut, cre, cph]
Maintainer: Yu Haichuan <yuhaichuan at whu.edu.cn>
BugReports: https://github.com/HaichuanYu0703/SPIMA/issues
License: MIT + file LICENSE
URL: https://github.com/HaichuanYu0703/SPIMA
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: spima citation info
Materials: README, NEWS
CRAN checks: spima results

Documentation:

Reference manual: spima.html , spima.pdf

Downloads:

Package source: spima_0.2.0.tar.gz
Windows binaries: r-devel: spima_0.2.0.zip, r-release: spima_0.2.0.zip, r-oldrel: spima_0.2.0.zip
macOS binaries: r-release (arm64): spima_0.2.0.tgz, r-oldrel (arm64): spima_0.2.0.tgz, r-release (x86_64): spima_0.2.0.tgz, r-oldrel (x86_64): spima_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=spima to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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