Package: multiColl 1.0

R. Salmeron

multiColl: Collinearity Detection in a Multiple Linear Regression Model

The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed. D.A. Belsley (1982) <doi:10.1016/0304-4076(82)90020-3>. D. A. Belsley (1991, ISBN: 978-0471528890). C. Garcia, R. Salmeron and C.B. Garcia (2019) <doi:10.1080/00949655.2018.1543423>. R. Salmeron, C.B. Garcia and J. Garcia (2018) <doi:10.1080/00949655.2018.1463376>. G.W. Stewart (1987) <doi:10.1214/ss/1177013444>.

Authors:R. Salmeron, C.B. Garcia and J. Garcia

multiColl_1.0.tar.gz
multiColl_1.0.zip(r-4.7)multiColl_1.0.zip(r-4.6)multiColl_1.0.zip(r-4.5)
multiColl_1.0.tgz(r-4.6-any)multiColl_1.0.tgz(r-4.5-any)
multiColl_1.0.tar.gz(r-4.7-any)multiColl_1.0.tar.gz(r-4.6-any)
multiColl_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
multiColl/json (API)

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

Bug tracker:https://r-forge.r-project.org/projects/colldetreat

Datasets:
  • KG - Klein and Goldberger data
  • theil - Henri Theil data

On CRAN:

Conda:

2.78 score 1 packages 20 scripts 245 downloads 14 exports 0 dependencies

Last updated from:4246eff3c5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK100
source / vignettesOK149
linux-release-x86_64OK105
macos-release-arm64OK96
macos-oldrel-arm64OK85
windows-develOK76
windows-releaseOK60
windows-oldrelOK62
wasm-releaseOK74

Exports:CNCNsCVCVskilumultiColmultiColLMperturbperturb.nPROPsRdetRSLMVIF

Dependencies: