Imports: lattice, stats, utils, grDevices
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.

Imports: lattice, stats, utils, grDevices, RColorBrewer
Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.

Title: Generalized Linear Mixed Models using Adaptive Gaussian
Description: Fits generalized linear mixed models for a single grouping factor under
maximum likelihood approximating the integrals over the random effects with an
adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995)

Authors@R: person("Dimitris", "Rizopoulos", email = "d.rizopoulos@erasmusmc.nl",
Suggests: lattice, knitr, rmarkdown, pkgdown, multcomp

Suggests: lattice, knitr, rmarkdown, pkgdown, multcomp, emmeans,

Imports: MASS, nlme, parallel, matrixStats

Package: GLMMarp
Version: 0.1-1
Date: 2009-09-28
Title: Generalized Linear Multilevel Model with AR(p) Errors Package
Author: Xun Pang <xpang@wustl.edu>
Maintainer: Xun Pang <xpang@wustl.edu>
Depends: R (>= 2.8.0), coda (>= 0.11-3), MCMCpack, mvtnorm, lattice,
msm, MASS, Matrix, accuracy, kinship, panel, bayesSurv
Description: This package contains functions to estimate the GLMM-AR(p)
model for analyzing discrete time-series cross-sectional data
via Markov Chain Monte Carlo simulation. The simulation is done
only with the R language. The model returns draws of the
parameter posteriors selected by the user in a list format.
Each parameter chain is returned as a matrix. The user is
responsible to summarize the mcmc output by using the coda
package. GLMMarp also contains several useful utility
functions, including an independent function for computing the
Bayes factor with GLMM-AR(p) output, a function to recover the
random coefficients at the individual level, and a function to
do prediction by using the posterior distributions. The package
also contains a library of supporting functions for the MCMC
simulation and Bayes factor estimation. In this version, no
tools for visualization are provided. To use the functions in
this package, the user needs to load the following two packages
by him/herself: \code{panel} and \code{bayesSurv}, because the
two packages have no namespace.
License: GPL
SystemRequirements: gcc (>= 4.0)
URL: http://xpang.wustl.edu/Computing.html
Repository: CRAN
Date/Publication: 2009-10-18 09:17:21
Packaged: 2009-10-16 17:15:53 UTC; xpang