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 <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] (HUIT), Boyu Ren [aut, cre] (McLean Hospital), Tanujit Dey [ctb] (HMS), Danielle Braun [aut] (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:

Reference manual:	GPCERF.pdf
Vignettes:	A-Note-on-Choosing-Hyperparameters Developers-Guide GPCERF Nearest-neighbor-Gaussian-Processes Standard Gaussian Processes

Downloads:

Package source:	GPCERF_0.2.4.tar.gz
Windows binaries:	r-devel: GPCERF_0.2.4.zip, r-release: GPCERF_0.2.4.zip, r-oldrel: GPCERF_0.2.4.zip
macOS binaries:	r-release (arm64): GPCERF_0.2.4.tgz, r-oldrel (arm64): GPCERF_0.2.4.tgz, r-release (x86_64): GPCERF_0.2.4.tgz, r-oldrel (x86_64): GPCERF_0.2.4.tgz
Old sources:	GPCERF archive

Linking:

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