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Using pharmacometrics to inform drug discovery and development decisions requires effective communication of models and data to all stakeholders. The new "vachette" visualization method presented here enables modelers and non-modelers to see how a model integrates and represents all data across relevant subgroups, showing how the model "sees" the data when it accounts for covariates. Vachette starts with user-provided model simulations ("curves"), together with observations used in creating (or otherwise relevant to) the model. It automatically produces a single, intuitive plot overlaying all observations onto a user-selected reference curve, accounting for covariate effects and preserving remaining random effects. The method automatically identifies characteristic landmarks (e.g., minima, maxima, and inflection points) which are used to split each curve into segments. A transformation on both x- and y-axes is then applied to each segment and its corresponding observations, accounting for covariate effects by aligning the segments to the reference, allowing intuitive visualization in one plot of covariate effects and of model fit to the data, preserving the distance between model predictions and the observations. Vachette-transformed data can also be used to enhance the utility of model assessments such as visual predictive checks and residual plots. Model visualizations using vachette enable easier and more effective evaluation and communication of the pharmacometric results critical to informing key decisions. Here the vachette method is described and its utility and flexibility are demonstrated through application to multiple types of pharmacometrics models, suggesting that vachette is a useful addition to the pharmacometrician's toolbox.