The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
| Version: | 1.6 |
| Depends: | R (≥ 2.10), MASS |
| Published: | 2023-10-21 |
| Author: | Haiyan Liu and Zhiyong Zhang |
| Maintainer: | Zhiyong Zhang <johnnyzhz at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | logistic4p results |
| Reference manual: | logistic4p.pdf |
| Package source: | logistic4p_1.6.tar.gz |
| Windows binaries: | r-devel: logistic4p_1.6.zip, r-release: logistic4p_1.6.zip, r-oldrel: logistic4p_1.6.zip |
| macOS binaries: | r-release (arm64): logistic4p_1.6.tgz, r-oldrel (arm64): logistic4p_1.6.tgz, r-release (x86_64): logistic4p_1.6.tgz, r-oldrel (x86_64): logistic4p_1.6.tgz |
| Old sources: | logistic4p archive |
Please use the canonical form https://CRAN.R-project.org/package=logistic4p to link to this page.
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.
Health stats visible at Monitor.