Generalized Additive Models (GAMs) are widely employed in ecological research, serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors. Nevertheless, evaluating the relative importance of predictors with concurvity (analogous to collinearity) on response variables in GAMs remains a challenge. To address this challenge, we developed an R package named . calculates individual values for predictors, based on the concept of 'average shared variance', a method previously introduced for multiple regression and canonical analyses. Through these individual s, which add up to the overall , researchers can evaluate the relative importance of each predictor within GAMs. We illustrate the utility of the package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London, UK. We believe that the package will improve the interpretation of results obtained from GAMs.