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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 |
| DOI: | 10.32614/CRAN.package.PLRModels |
| Author: | German Aneiros Perez and Ana Lopez-Cheda |
| Maintainer: | Ana Lopez-Cheda <ana.lopez.cheda at udc.es> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | PLRModels results |
| Reference manual: | PLRModels.pdf |
| Package source: | PLRModels_1.4.tar.gz |
| Windows binaries: | r-devel: PLRModels_1.4.zip, r-release: PLRModels_1.4.zip, r-oldrel: PLRModels_1.4.zip |
| macOS binaries: | r-release (arm64): PLRModels_1.4.tgz, r-oldrel (arm64): PLRModels_1.4.tgz, r-release (x86_64): PLRModels_1.4.tgz, r-oldrel (x86_64): PLRModels_1.4.tgz |
| Old sources: | PLRModels archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.