mclust, nnet, networkD3, boot, glasso, glmnet, jsonlite,
progress

boot, glasso, glmnet, jsonlite, mclust, mix, networkD3, nnet,
progress

Latent Unknown Clustering Integrating Multi-View Data
c(
person(given = "Yinqi", family = "Zhao",
email = "yinqiz@usc.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2413-732X")),
person(given = "David", family = "Conti",
email = "dconti@usc.med.edu", role = "ths",
comment = c(ORCID = "0000-0002-2941-7833")),
person(given = "Jesse", family = "Goodrich",
role = "ctb",
comment = c(ORCID = "0000-0001-6615-0472")),
person(given = "Cheng", family = "Peng", role = "ctb")
)
An implementation of the LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>).
LUCID conducts integrated clustering using exposures, omics data (and outcome
as an option). An EM algorithm is implemented to estimate MLE of the LUCID model.
LUCIDus features integrated variable selection, incorporation of missing omics
data, bootstrap inference, prediction and visualization of the model.
Yinqi Zhao [aut, cre] (<https://orcid.org/0000-0003-2413-732X>),
David Conti [ths] (<https://orcid.org/0000-0002-2941-7833>),
Jesse Goodrich [ctb] (<https://orcid.org/0000-0001-6615-0472>),
Cheng Peng [ctb]

Latent Variable Gaussian Process Modeling with Qualitative and
Quantitative Input Variables
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>.

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 input variables of the datasets can be quantitative, qualitative/categorical or mixed.
The output variable of the datasets is a scalar (quantitative).
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.