Visualize 'confounder' control in meta-analysis.
    'metaconfoundr' is an approach to evaluating bias in studies used in
    meta-analyses based on the causal inference framework. Study groups
    create a causal diagram displaying their assumptions about the
    scientific question. From this, they develop a list of important
    'confounders'. Then, they evaluate whether studies controlled for
    these variables well. 'metaconfoundr' is a toolkit to facilitate this
    process and visualize the results as heat maps, traffic light plots,
    and more.

Automatically estimate 14 effect size measures from a well-formatted dataset, including Cohen's d, Hedges' g, mean difference, odds ratio, risk ratio, incidence rate ratio, risk difference, number needed to treat, Pearson correlation, Fisher's z, Cronbach's alpha, intraclass correlation coefficient, and single-group proportion. Provides a two-tier quality-flag diagnostic system for input validation and post-computation plausibility checks, missing-data guidance that tells users which columns would unlock additional estimators, post-hoc correction for attenuation due to measurement error, and standalone psychometric utilities (standard error of measurement, smallest detectable change, change-score reliability). Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets.
    This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

Tools for the calculation of effect sizes (standardised mean difference) and mean difference in pre-post controlled studies, including robust imputation of missing variances (standard deviation of changes) and correlations (Pearson correlation coefficient). The main function 'metacor_dual()' implements several methods for imputing missing standard deviation of changes or Pearson correlation coefficient, and generates transparent imputation reports. Designed for meta-analyses with incomplete summary statistics. For details on the methods, see Higgins et al. (2023) and Fu et al. (2013).