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Package metaviz version 0.1.0

• February 6, 2017 first release on CRAN: https://CRAN.R-project.org/package=metaviz

Package metaviz version 0.1.1

• March 14, 2017: Added a new function for visual funnel plot inference: funnelinf. Also available as shiny app: https://metaviz.shinyapps.io/funnelinf_app/
• June 29, 2017: Version 0.1.1 submitted to CRAN

Package metaviz version 0.2

• March 16, 2018: Version 0.2 submitted to CRAN
• Added viz_funnel, a new function to create funnel plot variants (e.g., additional evidence contour enhanced funnel plots)
• Added viz_forest, a new function to create forest plot variants (classic, thick forest, and rainforest plots)
• Now includes an additional example dataset of a meta-analysis with dichotomous outcomes.

Package metaviz version 0.3

• January 14, 2019: Version 0.3 submitted to CRAN
• Added viz_sunset, a dedicated function for sunset (power-enhanced) funnel plots. Also available as shiny app: https://metaviz.shinyapps.io/sunset/
• Now includes an additional example dataset of a meta-analysis with standardized mean differences.

Package metaviz version 0.3.1

• April 7th, 2020: Version 0.3.1 submitted to CRAN
• Fixed problem of up-side-down funnel plots (viz_funnel, viz_sunset, funnelinf) with y_axis = “se”, which seemed to occur with newer versions of ggplot2 installed.
• Function viz_funnel now allows to customize funnel plot contours for random effects models via the contours_type argument.
• Changed behavior of viz_forest, viz_funnel and viz_sunset, such that when output of function rma.uni from package metafor is used as input, then the method argument is now extracted from the rma.uni object.

Package metaviz version 0.4.0

• Added new functions for visualization and assessment of within-study effect size inequality (wineq_forest, wineq_gini, and wineq_baujat).
• Added viz_tlma_forest for dedicated forest plots for three-level meta-analysis.
• Added viz_tlma_studyinfo for visualizing the distribution of effect sizes across studies in three-level meta-analysis.
• Fixed funnelinf erroring with n > 1 (including rorschach = TRUE), where the y-axis values were assigned from the original per-study standard errors instead of the replicated lineup data, causing a size-mismatch error.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.