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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# bayesnetworks
<!-- badges: start -->
[![Build Status](https://travis-ci.org/USCbiostats/bayesnetworks.svg?branch=master)](https://travis-ci.org/USCbiostats/bayesnetworks)
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/bayesnetworks)](https://cran.r-project.org/package=bayesnetworks)
<!-- badges: end -->
The goal of bayesnetworks is to do MCMC fitting of Bayesian networks, including source & sink constraints and external network info.
## Installation
<!--
You can install the released version of bayesnetworks from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("bayesnetworks")
```
-->
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("USCbiostats/bayesnetworks")
```
## Examples
The network is fitted by passing in data and network structure
```{r}
library(bayesnetworks)
set.seed(1234)
x <- bn_mcmc(X = network$data, graph = network$dag_info, N = 50000)
```
Some diagnostic plots:
```{r}
library(ggplot2)
ggplot(x, aes(iter, globalLL)) +
geom_line()
```
```{r}
ggplot(x, aes(iter, FN)) +
geom_line(aes(color = "FN")) +
geom_line(aes(y = FP, color = "FP")) +
labs(y = "value")
```
```{r}
ggplot(x, aes(iter, deletions)) +
geom_line(aes(color = "deletions")) +
geom_line(aes(y = additions, color = "additions")) +
labs(y = "count")
```
## Code of Conduct
Please note that the 'bayesnetworks' project is released with a [Contributor Code of Conduct](.github/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.