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.

Version: 1.1
Published: 2014-01-01
Author: German Aneiros Perez and Ana Lopez Cheda
Maintainer: German Aneiros Perez <ganeiros at udc.es>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: PLRModels results

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Reference manual: PLRModels.pdf
Package source: PLRModels_1.1.tar.gz
Windows binaries: r-devel: PLRModels_1.1.zip, r-release: PLRModels_1.1.zip, r-oldrel: PLRModels_1.1.zip
OS X El Capitan binaries: r-release: PLRModels_1.1.tgz
OS X Mavericks binaries: r-oldrel: PLRModels_1.1.tgz
Old sources: PLRModels archive

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