Data

Tools

Indexes

Applications

Experiments

Open Source Science

ccdrAlgorithm

Structure learning for Bayesian networks using the CCDr algorithm.

https://github.com/itsrainingdata/ccdralgorithm

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    9 of 15 committers (60.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.6%) to scientific vocabulary

Keywords

bayesian-networks experimental-data graphical-models machine-learning-algorithms r regularization statistical-learning
Last synced: 5 months ago · JSON representation

Repository

Structure learning for Bayesian networks using the CCDr algorithm.

Basic Info
  • Host: GitHub
  • Owner: itsrainingdata
  • Language: C++
  • Default Branch: master
  • Homepage:
  • Size: 298 KB
Statistics
  • Stars: 6
  • Watchers: 3
  • Forks: 9
  • Open Issues: 1
  • Releases: 5
Topics
bayesian-networks experimental-data graphical-models machine-learning-algorithms r regularization statistical-learning
Created almost 10 years ago · Last pushed about 2 years ago
Metadata Files
Readme

README.Rmd 

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

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

# ccdrAlgorithm

[![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/ccdrAlgorithm.svg?branch=master)](https://travis-ci.org/itsrainingdata/ccdrAlgorithm)
[![](http://www.r-pkg.org/badges/version/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)
[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/ccdrAlgorithm)](http://www.r-pkg.org/pkg/ccdrAlgorithm)

`ccdrAlgorithm` implements the CCDr structure learning algorithm described in [[1-2](#references)]. This algorithm estimates the structure of a Bayesian network from mixed observational and experimental data using penalized maximum likelihood based on L1 or concave (MCP) regularization.

Presently, this package implements the main algorithm and provides a method to simulate data from a Gaussian Bayesian network. To simulate random networks, it is recommended to use the [`sparsebnUtils`](https://cran.r-project.org/package=sparsebnUtils) package. Other packages for simulating DAGs and observational data include [`bnlearn`](https://cran.r-project.org/package=bnlearn), [`pcalg`](https://cran.r-project.org/package=pcalg), and [`igraph`](https://cran.r-project.org/package=igraph).

## Overview

The main method is `ccdr.run`, which runs the CCDr structure learning algorithm as described in [[1-2](#references)]. For simulating data from a Gaussian Bayesian network, the package provides the method `generate_mvn_data`. This method can simulate observational data or experimental data with interventions (or combinations of both).

## Installation

You can install:

* the latest CRAN version with

    ```R
    install.packages("ccdrAlgorithm")
    ````

* the latest development version from GitHub with

    ```R
    devtools::install_github(c("itsrainingdata/sparsebnUtils/dev", "itsrainingdata/ccdrAlgorithm/dev"))
    ```

## References

[1] Aragam, B. and Zhou, Q. (2015). [Concave penalized estimation of sparse Gaussian Bayesian networks.](http://jmlr.org/papers/v16/aragam15a.html) _The Journal of Machine Learning Research_. 16(Nov):2273−2328.

[2] Zhang, D. (2016). Concave Penalized Estimation of Causal Gaussian Networks with Intervention. Master’s thesis, UCLA.

[3] Fu, F. and Zhou, Q. (2013). [Learning sparse causal Gaussian networks with experimental intervention: Regularization and coordinate descent.](http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2012.754359) Journal of the American Statistical Association, 108: 288-300.

Owner

  • Name: Bryon Aragam
  • Login: itsrainingdata
  • Kind: user
  • Location: Chicago, IL
  • Company: University of Chicago

GitHub Events

Total
Last Year

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 290
  • Total Committers: 15
  • Avg Commits per committer: 19.333
  • Development Distribution Score (DDS): 0.555
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
itsrainingdata b****t@g****m 129
itsrainingdata Z****2@M****l 63
itsrainingdata Z****2@N****l 29
itsrainingdata Z****2@w****u 13
itsrainingdata Z****2@w****u 10
DachengZ s****8@g****m 8
itsrainingdata Z****2@w****u 8
itsrainingdata Z****2@M****l 5
itsrainingdata Z****2@w****u 5
itsrainingdata Z****2@w****u 5
itsrainingdata Z****2@w****u 5
itsrainingdata Z****2@M****n 4
itsrainingdata Z****2@w****u 3
itsrainingdata Z****2@w****u 2
itsrainingdata Z****2@w****u 1
Committer Domains (Top 20 + Academic)
wifi-131-179-20-118.host.ucla.edu: 1 wifi-131-179-20-58.host.ucla.edu: 1 wifi-131-179-21-252.host.ucla.edu: 1 macbook-pro.lan: 1 wifi-131-179-20-17.host.ucla.edu: 1 wifi-131-179-21-231.host.ucla.edu: 1 wifi-131-179-20-40.host.ucla.edu: 1 wifi-131-179-20-30.host.ucla.edu: 1 wifi-131-179-20-135.host.ucla.edu: 1 wifi-131-179-3-40.host.ucla.edu: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 3
  • Total pull requests: 2
  • Average time to close issues: 16 days
  • Average time to close pull requests: about 1 month
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • itsrainingdata (3)
Pull Request Authors
  • noriakis (2)
  • DachengZ (1)
Top Labels
Issue Labels
bug (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads: unknown
  • Total docker downloads: 21,255
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 8
cran.r-project.org: ccdrAlgorithm

CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 0
  • Docker Downloads: 21,255
Rankings
Forks count: 8.3%
Stargazers count: 21.1%
Dependent repos count: 25.5%
Dependent packages count: 29.8%
Average: 34.9%
Downloads: 89.7%
Last synced: 10 months ago
conda-forge.org: r-ccdralgorithm
  • Versions: 2
  • Dependent Packages: 1
  • Dependent Repositories: 0
Rankings
Dependent packages count: 28.8%
Dependent repos count: 34.0%
Average: 39.9%
Forks count: 43.4%
Stargazers count: 53.5%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.2.3 depends
  • Rcpp >= 0.11.0 imports
  • sparsebnUtils >= 0.0.5 imports
  • stats * imports
  • utils * imports
  • Matrix * suggests
  • graph * suggests
  • igraph * suggests
  • testthat * suggests