Package: spima 0.2.0

Yu Haichuan

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

Meta-analysis via 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.

Authors:Yu Haichuan

spima_0.2.0.tar.gz
spima_0.2.0.zip(r-4.7)spima_0.2.0.zip(r-4.6)spima_0.2.0.zip(r-4.5)
spima_0.2.0.tgz(r-4.6-x86_64)spima_0.2.0.tgz(r-4.6-arm64)spima_0.2.0.tgz(r-4.5-x86_64)spima_0.2.0.tgz(r-4.5-arm64)
spima_0.2.0.tar.gz(r-4.7-arm64)spima_0.2.0.tar.gz(r-4.7-x86_64)spima_0.2.0.tar.gz(r-4.6-arm64)spima_0.2.0.tar.gz(r-4.6-x86_64)
spima_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
spima/json (API)

# Install 'spima' in R:
install.packages('spima', repos = c('https://haichuanyu0703.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/haichuanyu0703/spima/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

3.00 score 27 exports 15 dependencies

Last updated from:3152ce8207. Checks:11 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING208
linux-devel-x86_64WARNING195
source / vignettesOK227
linux-release-arm64WARNING188
linux-release-x86_64WARNING172
macos-release-arm64WARNING115
macos-release-x86_64WARNING430
macos-oldrel-arm64WARNING111
macos-oldrel-x86_64WARNING353
windows-develWARNING145
windows-releaseWARNING122
windows-oldrelWARNING156
wasm-releaseOK143

Exports:forestpriorsmc_controlspimaspima_bin_analyzespima_bin_distancespima_bin_observed_statsspima_bin_simulatespima_bin_validatespima_cont_analyzespima_cont_distancespima_cont_observed_statsspima_cont_simulatespima_cont_validatespima_forestspima_gamma_analyzespima_gamma_distancespima_gamma_observed_statsspima_gamma_simulatespima_gamma_validatespima_generic_analyzespima_generic_distancespima_generic_observed_statsspima_generic_simulatespima_generic_validatespima_intspima_int_validate

Dependencies:bootlatticelme4MASSMatrixminqanlmenloptrrbibutilsRcppRcppArmadilloRcppEigenRdpackreformulasrlang

Readme and manuals

Help Manual

Help pageTopics
Blood Pressure Continuous Outcome Databp_cont
Distance Functions for ABC-SMCdistance_functions spima_bin_distance spima_cont_distance
Forest Plot for SPI-MA Resultsforest.spima spima_forest
Generic Effect Size Datagen_effect
Kidney Disease Binary Outcome Datakidney_bin
Evaluate log-prior density for a parameter vectorlog_prior_density
Plot Treatment Effect Modificationplot.spima_int
Define Prior Distributions for ABC-SMC Parametersprior
Run ABC-SMC Inferencerun_abc_smc
Sample from the joint priorsample_prior
Control Parameters for ABC-SMCsmc_control
spima: Simulated Pseudo-Individual Data Meta-Analysisspima
Analyze Pseudo-IPD for Binary Outcomespima_bin_analyze
Compute Observed Summary Statistics for Binary Dataspima_bin_observed_stats
Simulate Pseudo-IPD for Binary Outcomespima_bin_simulate
Validate Binary Outcome Inputspima_bin_validate
Analyze Pseudo-IPD for Continuous Outcomespima_cont_analyze
Compute Observed Summary Statistics for Continuous Dataspima_cont_observed_stats
Simulate Pseudo-IPD for Continuous Outcomespima_cont_simulate
Validate Continuous Outcome Inputspima_cont_validate
Analyze Pseudo-IPD for Gamma Outcomespima_gamma_analyze
Distance Function for Gamma Outcomespima_gamma_distance
Compute Observed Summary Statistics for Gamma Dataspima_gamma_observed_stats
Simulate Pseudo-IPD for Gamma Outcomespima_gamma_simulate
Validate Gamma Outcome Inputspima_gamma_validate
Analyze Pseudo-Data for Generic Effect-Sizespima_generic_analyze
Generic (effect-size) distance: weighted Euclidean distance on effectsspima_generic_distance
Compute Observed Summary Statistics for Generic Effect-Sizespima_generic_observed_stats
Simulate Pseudo-Data for Generic Effect-Sizespima_generic_simulate
Validate Generic Effect-Size Inputspima_generic_validate
SPI-MA Interaction Analysisspima_int spima_int_validate