Estimates models that extend the standard GLM to take misclassification into account. The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2015) < https://ftp.zew.de/pub/zew-docs/dp/dp15043.pdf>.
Estimates models that extend the standard GLM to take misclassification into account. The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2017) Computational Statistics & Data Analysis 110:145-159 http://dx.doi.org/10.1016/j.csda.2017.01.003.
The easiest way to use any of the functions in the misclassGLM package is to install the CRAN version. It can be installed from within R using the command:
#!R
install.packages("misclassGLM")
The devtools package contains functions that allow you to install R packages directly from bitbucket or github. If you've installed and loaded the devtools package, the installation command is
#!R
install_bitbucket("sdlugosz/misclassGLM")