NCGaussian: Nandram and Chen (1996) algorithm with Gaussian distribution...
In Bayesthresh: Bayesian thresholds mixed-effects models for categorical data
Description
Details
References
See Also
Description
This brings an implementation of Nandram and Chen (1996) algorithm adapted by Silva (2008)
for mixed models with inverse Gaussian distribution used as a link function for the latent variable
Details
Internal use
References
NANDRAM, B.; CHEN, M. H. (1996) Reparameterizing the generalized linear model to
accelerate gibbs sampler convergence, Journal of Statistical Computation and
Simulation, Abingdon, v. 54, p.129-144.
SILVA, J.W. (2008) Algorithms for thresholds models using Normal and Student-t cumulative distributions.
Thesis (Doctorate in Statistics and Agricultural Experimentation), University Federal of Lavras, p. 99.
See Also
Bayesthresh documentation built on May 30, 2017, 6:24 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details References See Also
Description
This brings an implementation of Nandram and Chen (1996) algorithm adapted by Silva (2008) for mixed models with inverse Gaussian distribution used as a link function for the latent variable
Details
Internal use
References
NANDRAM, B.; CHEN, M. H. (1996) Reparameterizing the generalized linear model to accelerate gibbs sampler convergence, Journal of Statistical Computation and Simulation, Abingdon, v. 54, p.129-144.
SILVA, J.W. (2008) Algorithms for thresholds models using Normal and Student-t cumulative distributions. Thesis (Doctorate in Statistics and Agricultural Experimentation), University Federal of Lavras, p. 99.
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.