Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Version: | 0.5.3 |
Depends: | R (≥ 3.3) |
Imports: | car, data.table, ggplot2, goftest, graphics, gridExtra, nortest, Rcpp, stats, utils |
LinkingTo: | Rcpp |
Suggests: | covr, descriptr, knitr, rmarkdown, testthat, vdiffr, xplorerr |
Published: | 2020-02-10 |
Author: | Aravind Hebbali [aut, cre] |
Maintainer: | Aravind Hebbali <hebbali.aravind at gmail.com> |
BugReports: | https://github.com/rsquaredacademy/olsrr/issues |
License: | MIT + file LICENSE |
URL: | https://olsrr.rsquaredacademy.com/, https://github.com/rsquaredacademy/olsrr |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | olsrr results |
Reference manual: | olsrr.pdf |
Vignettes: |
Heteroscedasticity Measures of Influence Introduction to olsrr Media Collinearity Diagnostics, Model Fit & Variable Contribution Residual Diagnostics Variable Selection Methods |
Package source: | olsrr_0.5.3.tar.gz |
Windows binaries: | r-devel: olsrr_0.5.3.zip, r-release: olsrr_0.5.3.zip, r-oldrel: olsrr_0.5.3.zip |
macOS binaries: | r-release: olsrr_0.5.3.tgz, r-oldrel: olsrr_0.5.3.tgz |
Old sources: | olsrr archive |
Reverse suggests: | xplorerr |
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