forked from cran/bark
/
README.Rmd
89 lines (62 loc) · 2.96 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# bark: Bayesian Additive Regression Kernels
<!-- badges: start -->
[](https://CRAN.R-project.org/package=bark)
[](https://github.com/merliseclyde/bark/actions)
[](https://app.codecov.io/gh/merliseclyde/bark)
[](https://bestpractices.coreinfrastructure.org/projects/7096)
<!-- badges: end -->
The bark package implements estimation for a Bayesian nonparametric regression model represented as a sum of multivariate Gaussian kernels as a flexible model to capture nonlinearities, interactions and feature selection.
## Installation
You can install the released version of bark [](https://cran.r-project.org/package=bark) from [CRAN](https://CRAN.R-project.org) with:
```{r eval=FALSE}
install.packages("bark")
```
And the development version from [GitHub](https://github.com/merliseclyde/bark) with:
```{r eval=FALSE}
require("devtools")
devtools::install_github("merliseclyde/bark")
```
(verify that the branch has a passing R CMD check badge above)
## Example
```{r example}
library(bark)
set.seed(42)
traindata <- sim_Friedman2(200, sd=125)
testdata <- sim_Friedman2(1000, sd=0)
fit.bark.d <- bark(y ~ .,
data=data.frame(traindata),
testdata = data.frame(testdata),
classification=FALSE,
selection = TRUE,
common_lambdas = FALSE,
printevery = 10^10)
mean((fit.bark.d$yhat.test.mean-testdata$y)^2)
```
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!