The R package 'effectsize' provides tools for estimating standardized effect sizes and their confidence intervals from various statistical models. It offers functions for calculating common effect sizes such as Cohen's d, Hedge's g, Glass' Δ, Pearson's φ, Cramér's V, and others. The package also includes functions for standardizing parameters and models, including robust standardization and pseudo-standardized coefficients for generalized linear mixed models. It supports effect sizes for ANOVAs, including Eta-squared, epsilon-squared, and omega-squared. The package also allows conversion between different types of effect sizes, such as from test statistics (F, t, χ², z) and between d, r, odds ratios, and risk ratios. Additionally, it provides functions for interpreting effect sizes according to established rules of thumb. The package is licensed under the GNU General Public License (v3.0) and is part of the easystats ecosystem. It is used by other R packages as a back-end for effect size computation. The package is available on GitHub and includes comprehensive documentation and examples.The R package 'effectsize' provides tools for estimating standardized effect sizes and their confidence intervals from various statistical models. It offers functions for calculating common effect sizes such as Cohen's d, Hedge's g, Glass' Δ, Pearson's φ, Cramér's V, and others. The package also includes functions for standardizing parameters and models, including robust standardization and pseudo-standardized coefficients for generalized linear mixed models. It supports effect sizes for ANOVAs, including Eta-squared, epsilon-squared, and omega-squared. The package also allows conversion between different types of effect sizes, such as from test statistics (F, t, χ², z) and between d, r, odds ratios, and risk ratios. Additionally, it provides functions for interpreting effect sizes according to established rules of thumb. The package is licensed under the GNU General Public License (v3.0) and is part of the easystats ecosystem. It is used by other R packages as a back-end for effect size computation. The package is available on GitHub and includes comprehensive documentation and examples.