This package provides statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
Authors: German Aneiros Perez <ganeiros@udc.es>
Ana Lopez Cheda <ana.lopez.cheda@udc.es>
Maintainer: Ana Lopez Cheda <ana.lopez.cheda@udc.es>
The most important functions are those directly related with the PLR models; that is, plrm.gcv, plrm.cv, plrm.beta, plrm.est, plrm.gof, plrm.ancova and plrm.ci. Although the other functions included in the package are auxiliary ones, they can be used independiently.