Title: Latent Variable Gaussian Process Modeling with Qualitative and
Description: Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (1D or 2D).
The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
The output variable of the datasets is a scalar (quantitative).
The method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>.

Description: Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.
Imports: lhs(>= 0.14), randtoolbox(>= 1.17)

Author: Siyu Tao, Yichi Zhang, Daniel W. Apley, Wei Chen

Package: LVMMCOR
Version: 0.01.1
Date: 2013-05-10
Title: A Latent Variable Model for Mixed Continuous and Ordinal
Responses
Author: Ehsan Bahrami Samani and Nourallah Tazikeh Miyandarreh
Maintainer: Ehsan Bahrami Samani <ehsan_bahrami_samani@yahoo.com>
Description: A model for mixed ordinal and continuous responses is
presented where the heteroscedasticity of the variance of the
continuous response is also modeled. In this model ordinal
response can be dependent on the continuous response. The aim
is to use an approach similar to that of Heckman (1978) for the
joint modelling of the ordinal and continuous responses. With
this model, the dependence between responses can be taken into
account by the correlation between errors in the models for
continuous and ordinal responses
Depends: R (>= 2.14.0),nlme,MASS
License: GPL (>= 2)
Contact: +982129902910
Packaged: 2013-06-05 19:35:59 UTC; StatisticS
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-06 07:49:30