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 <doi:10.48550/arXiv.2105.03454>.
Version: 0.2.4 Depends: R (≥ 3.5.0) Imports: parallel, xgboost, stats, MASS, spatstat.geom, logger, Rcpp, RcppArmadillo, ggplot2, cowplot, rlang, Rfast, SuperLearner, wCorr LinkingTo: RcppArmadillo, Rcpp Suggests: rmarkdown, knitr, testthat (≥ 3.0.0) Published: 2024-04-15 DOI: 10.32614/CRAN.package.GPCERF Author: Naeem Khoshnevis [aut] (AFFILIATION: HUIT), Boyu Ren [aut, cre] (AFFILIATION: McLean Hospital), Tanujit Dey [ctb] (AFFILIATION: HMS), Danielle Braun [aut] (AFFILIATION: HSPH) Maintainer: Boyu Ren <bren at mgb.org> BugReports: https://github.com/NSAPH-Software/GPCERF/issues License: GPL (≥ 3) Copyright: Harvard University URL: https://github.com/NSAPH-Software/GPCERF NeedsCompilation: yes Language: en-US Citation: GPCERF citation info Materials: README NEWS CRAN checks: GPCERF results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=GPCERF to link to this page.
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