Title: Tools for Gaussian Process Modeling in Uncertainty
Description: Gaussian processes (GPs) have been widely used to model spatial data, spatio-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, spatio-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on GPs with spatial data, spatio-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the Matérn family and the Confluent Hypergeometric family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo (MCMC) algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating GPs with all the implemented covariance functions; (5) unified implementation to allow easy specification of various GPs.
Imports: Rcpp (>= 1.0.1), stats, methods
SystemRequirements: GNU Scientific Library version >= 2.5

Depends: R (>= 2.7.0), randtoolbox, orthopolynom, ks, lars
Author: Miguel Munoz Zuniga and Jordan Ko
Description: A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.

Title: Gaussian Processes for Estimating Causal Exposure Response
person("Boyu", "Ren", email = "bren@partners.org",
comment = c(ORCID = "0000-0002-5300-1184", AFFILIATION="McLean Hospital")),
person("Tanujt", "Dey", email = "tdey@bwh.harvard.edu",
Description: Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <arXiv:2105.03454>.
Imports: parallel, data.table, xgboost, stats, MASS, spatstat.geom,