PLRModels: Statistical inference in partial linear regression models

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.

AuthorGerman Aneiros Perez and Ana Lopez Cheda
Date of publication2014-01-01 21:37:58
MaintainerGerman Aneiros Perez <>

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Man pages


np.ancova Man page Man page
np.est Man page
np.gcv Man page
np.gof Man page
par.ancova Man page Man page
par.est Man page
par.gof Man page
plrm.ancova Man page
plrm.beta Man page Man page Man page
plrm.est Man page
plrm.gcv Man page
plrm.gof Man page
PLRModels Man page
PLRModels-package Man page

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