Description: The metafor package is a comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbé, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010).
Description: The metadat package is an R data package that contains a large collection of meta-analysis datasets. These datasets are useful for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.
Description: By default, R only provides rudimentary support for the display of equations in the help pages (in the pdf manuals, full LaTeX support is available, but the vast majority of R users look at the HTML help pages, where only a few basic LaTeX commands are supported; for further details, see here). The mathjaxr package bundles MathJax and provides a number of macros that allow R package authors to make use of high-quality rendered equations and mathematical notation in their docs.
poolr: Package for Pooling the Results from (Dependent) Tests
Description: The poolr package contains functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher’s method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). For an introduction to the package, see Cinar and Viechtbauer (2022).
esmpack: A Package to Facilitate Preparation and Management of ESM/EMA Data
Description: The esmpack package is a collection of functions that facilitate preparation, management, visualization, and analysis of data collected via the experience sampling method (ESM) and ecological momentary assessment (EMA).