This easy-to-follow applied book on semiparametric regression methods using R is intended to
close the gap between the available methodology and its use in practice. Semiparametric
regression has a large literature but much of it is geared towards data analysts who have
advanced knowledge of statistical methods. While R now has a great deal of semiparametric
regression functionality many of these developments have not trickled down to rank-and-file
statistical analysts. The authors assemble a broad range of semiparametric regression R
analyses and put them in a form that is useful for applied researchers. There are chapters
devoted to penalized spines generalized additive models grouped data bivariate extensions of
penalized spines and spatial semi-parametric regression models. Where feasible the R code is
provided in the text however the book is also accompanied by an external website complete with
datasets and R code. Because of its flexibility semiparametric regression has proven to be of
great value with many applications in fields as diverse as astronomy biology medicine
economics and finance. This book is intended for applied statistical analysts who have some
familiarity with R.
