In this chapter, we reviewed applications of nonlinear methods in R using parametric and nonparametric methods for both theory-driven and exploratory analyses. As we reviewed, R has many excellent built-in functions for this, namely, nls, lm, ksmooth, and loess. There are additional functions available in a number of packages including KernSmooth and np. The np package is possibly the most capable of all packages discussed in this chapter but it offers this flexibility at the cost of high computational (and syntactic) resources, limiting its use in quickly exploring relationships in data.

In the subsequent chapters, we will focus on linear methods, first by reviewing linear algebra. This will be followed by two chapters on topics which make heavy use of linear algebra for model-based statistical analysis.