bark
bark R package for Bayesian nonparametric kernel regression
Science Score: 33.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
3 of 3 committers (100.0%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (16.6%) to scientific vocabulary
Keywords
bayesian
classification
cran-r
levy-processes
nonparametric-regression
prediction
regression
Last synced: 6 months ago
·
JSON representation
Repository
bark R package for Bayesian nonparametric kernel regression
Basic Info
- Host: GitHub
- Owner: merliseclyde
- License: gpl-3.0
- Language: R
- Default Branch: main
- Homepage: https://merliseclyde.github.io/bark/
- Size: 12.7 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
bayesian
classification
cran-r
levy-processes
nonparametric-regression
prediction
regression
Created over 1 year ago
· Last pushed over 1 year ago
https://github.com/merliseclyde/bark/blob/main/
# bark: Bayesian Additive Regression Kernels
[](https://github.com/merliseclyde/bark/actions)
[](https://codecov.io/github/merliseclyde/bark)
[](https://bestpractices.coreinfrastructure.org/projects/7096)
[](https://CRAN.R-project.org/package=bark)
[](https://doi.org/10.5281/zenodo.13894258)
The bark package implements estimation for a Bayesian nonparametric
regression model represented as a sum of multivariate Gaussian kernels
as a flexible model to capture nonlinearities, interactions and feature
selection.
## Installation
You can install the released version of bark
[](https://cran.r-project.org/package=bark)
from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("bark")
```
And the development version from
[GitHub](https://github.com/merliseclyde/bark) with:
``` r
require("devtools")
devtools::install_github("merliseclyde/bark")
```
(verify that the branch has a passing R CMD check badge above)
## Example
``` r
library(bark)
set.seed(42)
traindata <- sim_Friedman2(200, sd=125)
testdata <- sim_Friedman2(1000, sd=0)
fit.bark.d <- bark(y ~ .,
data=data.frame(traindata),
testdata = data.frame(testdata),
classification=FALSE,
selection = TRUE,
common_lambdas = FALSE,
printevery = 10^10)
mean((fit.bark.d$yhat.test.mean-testdata$y)^2)
#> [1] 1920.283
```
bark is similar to SVM, however it allows different kernel smoothing
parameters for every dimension of the inputs $x$ as well as selection of
inputs by allowing the kernel smoothing parameters to be zero.
The plot below shows posterior draws of the $\lambda$ for the simulated
data.
``` r
boxplot(as.data.frame(fit.bark.d$theta.lambda))
```
The posterior distribution for $\lambda_1$ and $\lambda_4$ are
concentrated near zero, which leads to $x_1$ and $x_2$ dropping from the
mean function.
## Roadmap for Future Enhancements
Over the next year the following enhancements are planned:
- port more of the R code to C/C++ for improvements in speed
- add S3 methods for `predict`, `summary`, `plot`
- add additional kernels and LARK methods from AOS (2011) paper
- better hyperparameter specification
If there are features you would like to see added, please feel free to
create an [issue in GitHub](https://github.com/merliseclyde/bark/issues)
and we can discuss!
Owner
- Name: Merlise Clyde
- Login: merliseclyde
- Kind: user
- Location: Durham, NC
- Company: Duke University
- Website: http://stat.duke.edu/~clyde
- Repositories: 8
- Profile: https://github.com/merliseclyde
Professor of Statistical Science, Duke University.
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Merlise Clyde | c****e@s****u | 119 |
| Merlise Clyde | c****e@d****u | 2 |
| Zhi Ouyang | z****2@s****u | 1 |
Committer Domains (Top 20 + Academic)
stat.duke.edu: 2
duke.edu: 1
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 9
- Total pull requests: 0
- Average time to close issues: 12 months
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.44
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: about 4 hours
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- merliseclyde (9)
Pull Request Authors
Top Labels
Issue Labels
enhancement (5)
bug (2)
help wanted (1)
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 260 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
cran.r-project.org: bark
Bayesian Additive Regression Kernels
- Homepage: https://www.R-project.org
- Documentation: http://cran.r-project.org/web/packages/bark/bark.pdf
- License: GPL (≥ 3)
-
Latest release: 1.0.5
published over 1 year ago
Rankings
Stargazers count: 28.5%
Forks count: 28.8%
Dependent packages count: 29.8%
Average: 30.9%
Downloads: 32.0%
Dependent repos count: 35.5%
Maintainers (1)
Last synced:
6 months ago