https://github.com/itsrainingdata/sparsebnutils

Utilities for learning sparse Bayesian networks
https://github.com/itsrainingdata/sparsebnutils

bayesian-networks graphical-models machine-learning r statistics

Last synced: 3 months ago
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Utilities for learning sparse Bayesian networks

• Host: GitHub
• URL: https://github.com/itsrainingdata/sparsebnutils
• Owner: itsrainingdata
• Created: 2016-02-02T02:05:24.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2019-05-29T11:08:50.000Z (about 6 years ago)
• Last Synced: 2025-03-27T03:22:32.872Z (4 months ago)
• Topics: bayesian-networks, graphical-models, machine-learning, r, statistics
• Language: R
• Size: 409 KB
• Stars: 2
• Watchers: 3
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output:

  md_document:

    variant: markdown_github

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# sparsebnUtils

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[![](http://www.r-pkg.org/badges/version/sparsebnUtils)](http://www.r-pkg.org/pkg/sparsebnUtils)

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A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data.

## Overview

This package provides various S3 classes for making it easy to estimate graphical models from data:

- `sparsebnData` for managing experimental data with interventions.

- `sparsebnFit` for representing the output of a DAG learning algorithm.

- `sparsebnPath` for representing a solution path of estimates.

The package also provides methods for manipulating these objects and for estimating parameters in graphical models:

- `estimate.parameters` for directed graphs.

- `get.precision` for undirected graphs.

- `get.covariance` for covariance matrices.