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
Published:
2024年04月15日
Author:
Naeem Khoshnevis
ORCID iD
[aut] (AFFILIATION: HUIT),
Boyu Ren
ORCID iD [aut,
cre] (AFFILIATION: McLean Hospital),
Tanujit Dey
ORCID iD [ctb]
(AFFILIATION: HMS),
Danielle Braun
ORCID iD
[aut] (AFFILIATION: HSPH)
Maintainer:
Boyu Ren <bren at mgb.org>
Copyright:
Harvard University
NeedsCompilation:
yes
Language:
en-US
Documentation:
Downloads:
Linking:
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