Bkmr
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Bcpd | 218 | 2 months ago | 9 | mit | C | |||||
| Bayesian Coherent Point Drift (BCPD/BCPD++/GBCPD/GBCPD++) | ||||||||||
| Personalizedmultitasklearning | 53 | 4 years ago | Python | |||||||
| Code for performing 3 multitask machine learning methods: deep neural networks, Multitask Multi-kernel Learning (MTMKL), and a hierarchical Bayesian model (HBLR). | ||||||||||
| Approxbayes.jl | 44 | a year ago | 12 | other | Julia | |||||
| Approximate Bayesian Computation (ABC) algorithms for likelihood free inference in julia | ||||||||||
| Simpleabc | 29 | 7 years ago | mit | Jupyter Notebook | ||||||
| A Python package for Approximate Bayesian Computation | ||||||||||
| Bkmr | 23 | a year ago | 7 | R | ||||||
| Bayesian kernel machine regression | ||||||||||
| Kerneldensityestimate.jl | 22 | 10 months ago | 10 | lgpl-2.1 | Julia | |||||
| Kernel Density Estimate with product approximation using multiscale Gibbs sampling | ||||||||||
| Gpsig | 14 | 3 years ago | 1 | apache-2.0 | Jupyter Notebook | |||||
| Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | ||||||||||
| Bmtmkl | 13 | 5 years ago | R | |||||||
| Bayesian Multitask Multiple Kernel Learning | ||||||||||
| Bayesian Ntk | 13 | 2 years ago | Jupyter Notebook | |||||||
| Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel' | ||||||||||
| Herding Paper | 11 | 7 years ago | mit | TeX | ||||||
| Optimally-weighted herding is Bayesian Quadrature | ||||||||||
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Readme
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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