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The R package bkmr implements Bayesian kernel machine regression, a statistical approach for estimating the joint health effects of multiple concurrent exposures. Additional information on the statistical methodology and on the computational details are provided in Bobb et al. 2015. More recent extensions, details on the software, and worked-through examples are provided in Bobb et al. 2018.

You can install the latest released version of bkmr from CRAN with:

install.packages("bkmr")

Or the latest development version from github with:

install.packages("devtools")
devtools::install_github("jenfb/bkmr")

For a general overview and guided examples, go to https://jenfb.github.io/bkmr/overview.html.

For examples from the software paper, please see

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Install

install.packages('bkmr')

Monthly Downloads

1,088

Version

0.2.2

License

GPL-2

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Maintainer

Jennifer F. Bobb

Last Published

March 28th, 2022

Functions in bkmr (0.2.2)

ExtractPIPs

Extract posterior inclusion probabilities (PIPs) from BKMR model fit
PredictorResponseBivarPair

Plot bivariate predictor-response function on a new grid of points
ComputePostmeanHnew

Compute the posterior mean and variance of h at a new predictor values
PredictorResponseBivar

Predict the exposure-response function at a new grid of points
ExtractEsts

Extract summary statistics
PredictorResponseBivarLevels

Plot cross-sections of the bivariate predictor-response function
ExtractSamps

Extract samples
PlotPriorFits

Plot of exposure-response function from univariate KMR fit
InvestigatePrior

Investigate prior
kmbayes

Fit Bayesian kernel machine regression
OverallRiskSummaries

Calculate overall risk summaries
SimData

Simulate dataset
SingVarIntSummaries

Single Variable Interaction Summaries
SingVarRiskSummaries

Single Variable Risk Summaries
TracePlot

Trace plot
print.bkmrfit

Print basic summary of BKMR model fit
summary.bkmrfit

Summarizing BKMR model fits
PredictorResponseUnivar

Plot univariate predictor-response function on a new grid of points
SamplePred

Obtain posterior samples of predictions at new points