GPCERF: Gaussian Processes for Estimating Causal Exposure Response Curves

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>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: parallel, data.table, xgboost, stats, MASS, spatstat.geom, logger, Rcpp, ggplot2, rlang, Matrix
LinkingTo: RcppArmadillo, Rcpp
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Published: 2022-07-02
Author: Naeem Khoshnevis ORCID iD [aut, cre] (FASRC), Boyu Ren ORCID iD [aut] (McLean Hospital), Tanujt Dey ORCID iD [ctb] (HMS), Danielle Braun ORCID iD [aut] (HSPH)
Maintainer: Naeem Khoshnevis <nkhoshnevis at>
License: GPL (≥ 3)
Copyright: Harvard University
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: GPCERF results


Reference manual: GPCERF.pdf
Vignettes: Full Gaussian Processes
Nearest-neighbor Gaussian Processes


Package source: GPCERF_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GPCERF_0.1.0.tgz, r-oldrel (arm64): GPCERF_0.1.0.tgz, r-release (x86_64): GPCERF_0.1.0.tgz, r-oldrel (x86_64): GPCERF_0.1.0.tgz


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