Partial Eta-Squared for Crossed, Nested, and Mixed Linear Mixed Models // Computes partial eta-squared effect sizes for fixed effects in linear mixed models fitted with the 'lme4' package. Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Factor predictors are supported directly, and a single factor-level (omnibus) effect size can be obtained for a multi-level factor or multi-df interaction. Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided, with optional parametric bootstrap confidence intervals. For correlated predictors, per-predictor effect sizes use unique (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) doi:10.1016/j.tics.2019.12.009, Correll, Mellinger, and Pedersen (2022) doi:10.3758/s13428-021-01687-2, and Rights and Sterba (2019) doi:10.1037/met0000184.