Package: PLRModels 1.4

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.

Authors:German Aneiros Perez and Ana Lopez-Cheda

PLRModels_1.4.tar.gz
PLRModels_1.4.zip(r-4.7-any)PLRModels_1.4.zip(r-4.6-any)PLRModels_1.4.zip(r-4.5-any)
PLRModels_1.4.tgz(r-4.6-any)PLRModels_1.4.tgz(r-4.5-any)
PLRModels_1.4.tar.gz(r-4.7-any)PLRModels_1.4.tar.gz(r-4.6-any)
PLRModels_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PLRModels/json (API)

# Install 'PLRModels' in R:
install.packages('PLRModels', repos = c('https://analopezcheda.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.26 score 18 scripts 289 downloads 16 exports 0 dependencies

Last updated from:0a13da2e29. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK124
source / vignettesOK118
linux-releaseOK135
macos-releaseOK86
macos-oldrelOK125
windows-develOK70
windows-releaseOK65
windows-oldrelOK68
wasm-releaseOK88

Exports:np.ancovanp.cvnp.estnp.gcvnp.gofpar.ancovapar.cipar.estpar.gofplrm.ancovaplrm.betaplrm.ciplrm.cvplrm.estplrm.gcvplrm.gof

Dependencies: