Title: Global and Individual Tests for Direct Effects
Description: Functions evaluate global and individual tests for direct effects in Mendelian randomization studies.

Description: Functions evaluate global and individual tests for direct effects in Mendelian randomization studies. Methods used in the package refer to J. Y. Dai, U. Peters, X. Wang, J. Kocarnik et al. (2018) <doi:10.1093/aje/kwy177>.

Description: Global and individual tests for pleiotropy and direct effects in Mendelian randomization studies. Refer to J. Y. Dai, U. Peters, X. Wang, J. Kocarnik et al. AJE (2018) <doi:10.1093/aje/kwy177>.

Package: GLMMGibbs
Version: 0.5-1
Author: Jonathan Myles and David Clayton <mylesj@icrf.icnet.uk>
Maintainer: Jonathan Myles <mylesj@icrf.icnet.uk>
Depends: R (>= 1.0)
Date: 2001/22/01
Title: Generalised Linear Mixed Models by Gibbs Sampling
Description: Generalised Linear Mixed Models are an extension of
Generalised Linear Models to include non-independent responses.
This package allows them to be fitted by including functions
for declaring factors to be random effects, for fitting models
and generic functions for examining the fits.
License: GPL version 2 or later

Package: GLMMGibbs
Version: 0.5
Author: Jonathan Myles and David Clayton <mylesj@icrf.icnet.uk>
Maintainer: Jonathan Myles <mylesj@icrf.icnet.uk>
Depends: R (>= 1.0)
Date: 2001/22/01
Title: Generalised Linear Mixed Models by Gibbs Sampling
Description: Generalised Linear Mixed Models are an extension of
Generalised Linear Models to include non-independent responses.
This package allows them to be fitted by including functions
for declaring factors to be random effects, for fitting models
and generic functions for examining the fits.
License: GPL version 2 or later

Title: GLM for Binary Randomized Response Data
Author: Jean-Paul Fox [aut], Konrad Klotzke [aut], Duco Veen [aut]
Description: GLM for Binary Randomized Response Data. Includes Cauchit, Log-log,
Logistic, and Probit link functions for Bernoulli distributed RR data.

Title: Generalized Linear Mixed Model (GLMM) for Binary Randomized
Description: Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
Includes Cauchit, Log-Log, Logistic, and Probit link functions for Bernoulli distributed RR data.