PLRModels: Statistical Inference in Partial Linear Regression Models

Contains 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: 1.4
Imports: stats
Published: 2023-08-19
Author: German Aneiros Perez and Ana Lopez-Cheda
Maintainer: Ana Lopez-Cheda <ana.lopez.cheda at>
License: GPL-3
NeedsCompilation: no
CRAN checks: PLRModels results


Reference manual: PLRModels.pdf


Package source: PLRModels_1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PLRModels_1.4.tgz, r-oldrel (arm64): PLRModels_1.4.tgz, r-release (x86_64): PLRModels_1.4.tgz
Old sources: PLRModels archive


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