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 evolutionary studies.