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R-native meta-analysis via Harrer et al. (2021) — effect sizes, pooling, forest/funnel plots, heterogeneity, subgroup, meta-regression, publication bias (Egger, trim-and-fill, PET-PEESE, p-curve), NMA, Bayesian MA, SEM-MA, RoB plots, power analysis.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Microsoft Identity and Access Administrator (SC-300) grounded skill. Covers Entra ID tenant management, user/group/device identities, hybrid identity, authentication methods (MFA, passkeys, FIDO2, certificate-based), Conditional Access, ID Protection, Global Secure Access, workload identities (managed identities, service principals, app registrations), enterprise app integration, identity governance (PIM, access reviews, entitlement management), and monitoring (KQL, workbooks, Identity Secure Score). Use when configuring Entra ID, managing authentication, setting up Conditional Access, implementing managed identities, reviewing access, or troubleshooting identity issues.
Master skill bundle for InsightPulseAI. Covers Azure resource constants (ipai-resource-map), Odoo 18 CE development conventions (ipai-odoo-platform), Pulser agent platform patterns (ipai-agent-platform), and Odoo docs enhancement. Load for any IPAI Azure, Odoo, Foundry, or agent work. Triggers on: IPAI, InsightPulseAI, Pulser, Odoo 18 CE, ipai_*, BIR compliance, ACA deployment, Foundry, rg-ipai, pg-ipai-odoo, ipai-copilot-resource.
Deploy and configure Microsoft's SaaS Accelerator for marketplace offers. Pre-built landing page, fulfillment webhook, subscription management — eliminates custom development for marketplace technical integration.
End-to-end guide for publishing a SaaS offer on Microsoft AppSource/Azure Marketplace. Covers Partner Center setup, offer creation, technical integration, listing content, and certification. Based on Microsoft's Mastering the Marketplace curriculum.
Extract structured data from research papers using Azure Document Intelligence and GPT-4.1 vision. Automates PICO extraction, risk of bias assessment, and data table population.
Perform meta-analyses using Azure Databricks for computation and Power BI for visualization. Supports fixed/random effects, subgroup analysis, and publication bias assessment.
| name | doing-meta-analysis-r |
| domain | research |
| version | 1.0.0 |
| license | CC0-1.0 |
| language | R |
| description | R-native meta-analysis via Harrer et al. (2021) — effect sizes, pooling, forest/funnel plots, heterogeneity, subgroup, meta-regression, publication bias (Egger, trim-and-fill, PET-PEESE, p-curve), NMA, Bayesian MA, SEM-MA, RoB plots, power analysis. |
| triggers | ["meta-analysis","forest plot","heterogeneity","I-squared","tau-squared","Egger","trim and fill","meta-regression","network meta-analysis","Harrer","dmetar","metafor","systematic review pooling","effect size pooling"] |
| sources | ["https://github.com/MathiasHarrer/Doing-Meta-Analysis-in-R","https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/"] |
| citation | Harrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D. (2021). Doing Meta-Analysis with R: A Hands-On Guide. Chapman & Hall/CRC Press. ISBN 978-0-367-61007-4. |
Canonical reference: Harrer et al. (2021) Doing Meta-Analysis with R: A Hands-On Guide.
Invoke when the user asks for any of: effect-size pooling, forest/funnel plots, heterogeneity diagnostics, subgroup / meta-regression, publication-bias assessment (Egger, trim-and-fill, PET-PEESE, p-curve), network meta-analysis, Bayesian meta-analysis, SEM-MA, RoB plots, or power analysis.
install.packages(c("meta", "metafor", "netmeta", "brms"))
# dmetar is a companion package — bundled with the Harrer book
remotes::install_github("MathiasHarrer/dmetar")
esc / metafor::escalc() to compute effect sizes (SMD, OR, HR, RR).meta::metagen() (generic IV), metabin(), metacont(), metacor().
Default: REML random-effects; report τ², I², prediction interval.meta::forest(), funnel(); save to PDF/PNG at 300 DPI.update.meta(subgroup=), meta-regression via metafor::rma().metabias() (Egger), trimfill(), PET-PEESE (metafor),
p-curve (dmetar::pcurve()).netmeta::netmeta(), SUCRA via netrank().brms::brm(bf(yi | se(sei) ~ 1 + (1|study))).prisma2020-flow-diagram.stipaidevlake/research/<study_id>/) and register metadata in ops.artifacts.meta-analysis-pipeline (orchestration), prisma2020-flow-diagram (reporting).study_id, n1, n2, mean1, mean2, sd1, sd2 (continuous) or
study_id, events1, n1, events2, n2 (binary).