Bayesian Kernel Machine Regression

Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures.


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.

You can install the latest releasted 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.

News

bkmr 0.2.0

Major changes

  • Added ability to have binomial outcome family by implementing probit regression within kmbayes()

  • Removed computation of the subject-specific effects h[i] within kmbayes(), as this is not always desired, and greatly slows down model fitting

    • This could still be done by setting the option est.h = TRUE in the kmbayes function

    • posterior samples of h[i] can now be obtained via the post-processing SamplePred function; alternatively, posterior summaries (mean, variance) can be obtained via the post-processing ComputePostmeanHnew function

  • Added ability to use exact estimates of the posterior mean and variance by specifying the argument method = 'exact' within the post-processing functions (e.g., OverallRiskSummaries(), PredictorResponseUnivar())

Bug fixes

  • Fixed PredictorResponseBivarLevels() when argument both_pairs = TRUE (#4)

Reference manual

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install.packages("bkmr")

0.2.0 by Jennifer F. Bobb, a year ago


https://github.com/jenfb/bkmr


Report a bug at https://github.com/jenfb/bkmr/issues


Browse source code at https://github.com/cran/bkmr


Authors: Jennifer F. Bobb [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports dplyr, magrittr, nlme, fields, truncnorm, tidyr, MASS, tmvtnorm


See at CRAN