README.Rmd

---
output:
  md_document:
    variant: markdown_github
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# sparsebnUtils

[![Project Status: Active  The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
[![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/sparsebnUtils.svg?branch=master)](https://travis-ci.org/itsrainingdata/sparsebnUtils)
[![](http://www.r-pkg.org/badges/version/sparsebnUtils)](http://www.r-pkg.org/pkg/sparsebnUtils)
[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/sparsebnUtils)](http://www.r-pkg.org/pkg/sparsebnUtils)

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.