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>.
- Version0.2.4
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- GPCERF citation info
- Last release04/15/2024
Documentation
Team
Boyu Ren
Naeem Khoshnevis
Show author detailsRolesAuthorTanujit Dey
Show author detailsRolesContributorDanielle Braun
Insights
Last 30 days
This package has been downloaded 266 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,612 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Apr 17, 2024 with 83 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports12 packages
- Suggests3 packages
- Linking To2 packages