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

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>.

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>.

Jonathan Myles and David Clayton <mylesj@icrf.icnet.uk>
Generalised Linear Mixed Models by Gibbs Sampling
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.

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

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

Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.

lattice, stats, utils, grDevices, RColorBrewer
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.