Title: Generalized Partially Linear Tree-based Regression Model
Author: Cyprien Mbogning <cyprien.mbogning@inserm.fr> and Wilson Toussile
Description: Package for generalized partially linear tree-based regression model, combining a
generalized linear model with an additional tree part on the same scale.

Description: Combining a generalized linear model with an additional tree part
on the same scale. A four-step procedure is proposed to fit the model and test
the joint effect of the selected tree part while adjusting on confounding factors.
We also proposed an ensemble procedure based on the bagging to improve prediction
accuracy and computed several scores of importance for variable selection.

Title: Generalized Partially Linear Tree-Based Regression Model

Title: Gaussian Process Modeling of Multi-Response Datasets
Description: Provides a general and efficient tool for fitting a response surface to datasets via Gaussian processes. The dataset can have multiple responses and the fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.

Author: Ramin Bostanabad, Wei Chen (IDEAL)

Description: Provides a general and efficient tool for fitting a response surface to datasets via Gaussian processes. The dataset can have multiple responses. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S., Apley, D. W. & Chen, W. Leveraging the nugget parameter for efficient Gaussian process modeling (2017) <doi:10.1002/nme.5751>.

Description: A general and efficient package for modeling (possibly noisy) datasets via Gaussian processes.

Title: Gaussian Process Modeling of Multi-Response and Possibly Noisy
Author: Ramin Bostanabad, Tucker Kearney, Siyo Tao, Daniel Apley, and Wei Chen (IDEAL)
Description: Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.