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 [aut, cre] (FASRC), Boyu Ren [aut] (McLean Hospital), Tanujt Dey [ctb] (HMS), Danielle Braun [aut] (HSPH)
Maintainer:	Naeem Khoshnevis <nkhoshnevis at g.harvard.edu>
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
Materials:	README NEWS
CRAN checks:	GPCERF results

Documentation:

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

Downloads:

Package source:	GPCERF_0.1.0.tar.gz
Windows binaries:	r-devel: GPCERF_0.1.0.zip, r-release: GPCERF_0.1.0.zip, r-oldrel: GPCERF_0.1.0.zip
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

Linking:

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