Package: GPCERF 0.2.4
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>.
Authors:
GPCERF_0.2.4.tar.gz
GPCERF_0.2.4.zip(r-4.5)GPCERF_0.2.4.zip(r-4.4)GPCERF_0.2.4.zip(r-4.3)
GPCERF_0.2.4.tgz(r-4.4-x86_64)GPCERF_0.2.4.tgz(r-4.4-arm64)GPCERF_0.2.4.tgz(r-4.3-x86_64)GPCERF_0.2.4.tgz(r-4.3-arm64)
GPCERF_0.2.4.tar.gz(r-4.5-noble)GPCERF_0.2.4.tar.gz(r-4.4-noble)
GPCERF_0.2.4.tgz(r-4.4-emscripten)GPCERF_0.2.4.tgz(r-4.3-emscripten)
GPCERF.pdf |GPCERF.html✨
GPCERF/json (API)
NEWS
# Install 'GPCERF' in R: |
install.packages('GPCERF', repos = c('https://nsaph-software.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nsaph-software/gpcerf/issues
Last updated 6 months agofrom:e38b5a0456. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win-x86_64 | OK | Nov 02 2024 |
R-4.5-linux-x86_64 | OK | Nov 02 2024 |
R-4.4-win-x86_64 | OK | Nov 02 2024 |
R-4.4-mac-x86_64 | OK | Nov 02 2024 |
R-4.4-mac-aarch64 | OK | Nov 02 2024 |
R-4.3-win-x86_64 | OK | Nov 02 2024 |
R-4.3-mac-x86_64 | OK | Nov 02 2024 |
R-4.3-mac-aarch64 | OK | Nov 02 2024 |
Exports:compute_rl_deriv_gpcompute_rl_deriv_nncompute_w_correstimate_cerf_gpestimate_cerf_nngpestimate_gpsgenerate_synthetic_dataget_loggerset_logger
Dependencies:bitopscaToolsclicodetoolscolorspacecowplotcvAUCdata.tabledeldirfansifarverforeachgamggplot2gluegplotsgtablegtoolsisobanditeratorsjsonliteKernSmoothlabelinglatticelifecycleloggermagrittrMASSMatrixmgcvminqamnormtmunsellnlmennlspillarpkgconfigpolyclipR6RColorBrewerRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastrlangROCRscalesspatstat.dataspatstat.geomspatstat.univarspatstat.utilsSuperLearnertibbleutf8vctrsviridisLitewCorrwithrxgboost
A Note on Choosing Hyperparameters
Rendered fromA-Note-on-Choosing-Hyperparameters.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2023-11-21
Started: 2023-11-21
Developers Guide
Rendered fromDevelopers-Guide.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2023-03-15
Started: 2022-04-21
GPCERF
Rendered fromGPCERF.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2023-08-11
Started: 2022-04-21
Nearest-neighbor Gaussian Processes
Rendered fromNearest-neighbor-Gaussian-Processes.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2023-08-11
Started: 2022-07-05
Standard Gaussian Processes
Rendered fromStandard-Gaussian-Processes.Rmd
usingknitr::rmarkdown
on Nov 02 2024.Last update: 2023-08-11
Started: 2023-03-15
Readme and manuals
Help Manual
Help page | Topics |
---|---|
The 'GPCERF' package. | GPCERF-package GPCERF |
Detect change-point in standard GP | compute_rl_deriv_gp |
Calculate right minus left derivatives for change-point detection in nnGP | compute_rl_deriv_nn |
Compute weighted covariate balance | compute_w_corr |
Estimate the conditional exposure response function using Gaussian process | estimate_cerf_gp |
Estimate the conditional exposure response function using nearest neighbor Gaussian process | estimate_cerf_nngp |
Estimate a model for generalized propensity score | estimate_gps |
Generate synthetic data for the GPCERF package | generate_synthetic_data |
Get logger settings | get_logger |
Extend generic plot functions for cerf_gp class | plot.cerf_gp |
Extend generic plot functions for cerf_nngp class | plot.cerf_nngp |
Extend print function for cerf_gp object | print.cerf_gp |
Extend print function for cerf_nngp object | print.cerf_nngp |
Set logger settings | set_logger |
print summary of cerf_gp object | summary.cerf_gp |
print summary of cerf_nngp object | summary.cerf_nngp |