In both theoretical and applied research, it is often of interest to assess the strength of an observed association. This is typically done to allow the judgment of the magnitude of an effect (especially when units of measurement are not meaningful, e.g., in the use of estimated latent variables; Bollen, 1989), to facilitate comparing between predictors' importance within a given model, or both. Though some indices of effect size, such as the correlation coefficient (itself a standardized covariance coefficient) are readily available, other measures are often harder to obtain. effectsize is an R package (R Core Team, 2020) that fills this important gap, providing utilities for easily estimating a wide variety of standardized effect sizes (i.e., effect sizes that are not tied to the units of measurement of the variables of interest) and their confidence intervals (CIs), from a variety of statistical models. effectsize provides easy-to-use functions, with full documentation and explanation of the various effect sizes offered, and is also used by developers of other R packages as the back-end for effect size computation, such as parameters (Lüdecke et al., 2020), ggstatsplot (Patil, 2018), gtsummary (Sjoberg et al., 2020) and more. Comparison to Other Packages effectsize's functionality is in part comparable to packages like lm.beta (Behrendt, 2014), MOTE (Buchanan et al., 2019), and MBESS (K. Kelley, 2020). Yet, there are some notable differences, e.g.: • lm.beta provides standardized regression coefficients for linear models, based on post-hoc model matrix standardization. However, the functionality is available only for a limited number of models (models inheriting from the lm class), whereas effectsize provides support for many types of models, including (generalized) linear mixed models, Bayesian models, and more. Additionally, in additional to post-hoc model matrix standardization, effectsize offers other methods of standardization (see below). • Both MOTE and MBESS provide functions for computing effect sizes such as Cohen's d and effect sizes for ANOVAs (Cohen, 1988), and their confidence intervals. However, both require manual input of For t-statistics, degrees of freedom, and sums of squares for the computation the effect sizes, whereas effectsize can automatically extract this information from the provided models, thus allowing for better ease-of-use as well as reducing any potential for error.