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1. Nonlinear, parametric curve‐fitting provides a framework for understanding diverse ecological and evolutionary trends (e.g. growth patterns and seasonal cycles). Currently, parametric curve‐fitting requires a priori assumptions of curve trajectories, restricting their use for exploratory analyses. Furthermore, use of analytical techniques [nonlinear least‐squares (NLS) and nonlinear mixed‐effects models] for complex parametric curves requires efficient choice of starting parameters.

2. We illustrate the new R package FlexParamCurve that automates curve selection and provides tools to analyse nonmonotonic curve data in NLS and nonlinear mixed‐effects models. Examples include empirical and simulated data sets for the growth of seabird chicks.

3. By automating curve selection and parameterization during curve‐fitting, FlexParamCurve extends current possibilities for parametric analysis in ecological and …