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"This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian meta-analysis approaches, SEM meta-analysis are also covered. A companion R package, dmetar, is introduced in the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide"--

Meta-Analysis --- R (Computer program language) --- Meta-analysis. --- Meta-analysis --- Medicine --- Psychometrics --- Social sciences --- GNU-S (Computer program language) --- Domain-specific programming languages --- Research --- Evaluation --- Statistical methods