Meta Analysis and Research Synthesis • mars

mars provides tools for meta-analysis and research synthesis in R, with support for univariate, multivariate, and multilevel meta-analytic models. It also includes helpers for within-study variance-covariance matrices, path analysis after correlation synthesis, publication-bias models, diagnostics, network meta-analysis, reporting, and exploratory random-forest meta-analysis.

Installation

Install the released version from CRAN with:

install.packages("mars")

The package can be installed directly from GitHub using devtools or the remotes package with the following command:

remotes::install_github("lebebr01/mars")

To install the package vignettes, add the build_vignettes argument:

remotes::install_github("lebebr01/mars",
                        build_vignettes = TRUE)

Quick Example

A univariate random-effects meta-analysis can be fit with mars() by supplying an effect-size column, a sampling-variance column, and a study identifier:

library(mars)

fit <- mars(
  data = teacher_expectancy,
  studyID = "study",
  effectID = NULL,
  sample_size = NULL,
  formula = yi ~ 1,
  variance = "vi",
  varcov_type = "univariate",
  structure = "univariate"
)

summary(fit)

Choosing a workflow

Most analyses start with mars(), but the package also includes higher-level helpers for common research-synthesis tasks:

Goal	Start with	Learn more
Estimate an average effect, meta-regression, or dependent-effects model	`mars()`	`vignette("MARS-Model-Examples", package = "mars")`
Choose among core workflows	`vignette("Choosing-a-MARS-Workflow", package = "mars")`	Workflow map
Build within-study variance-covariance matrices	`mars()` with `varcov_type`	`vignette("Within-Study-VarCov-Metrics", package = "mars")`
Diagnose residuals, influence, or heterogeneity	`residual_diagnostics()`, `influence_diagnostics()`, `profile_random_effects()`	`vignette("Residual-Diagnostics-Workflow", package = "mars")`
Assess publication bias or sensitivity	`publication_bias()`, `mars_alt_estimation()`	Publication-bias and alternative-estimation vignettes
Fit treatment networks	`network_meta()`	`vignette("Network-Meta-Analysis", package = "mars")`
Fit path, EFA, or CFA models after correlation synthesis	`path_model()`, `efa_from_synthesis()`, `cfa_from_synthesis()`	Latent-factor and synthesis-report vignettes
Create review and reporting graphics	`prisma_diagram()`, `risk_of_bias_plot()`, `gap_map_plot()`	Plot-specific vignettes
Explore nonlinear moderator patterns	`mars_rf()`	`vignette("Random-Forest-Meta-Analysis", package = "mars")`

Explore articles

The installed package includes core vignettes. Additional workflow articles are built for the pkgdown site.

Available installed vignettes can be accessed with the following code:

vignette(package = 'mars')