Nothing
ccdrAlgorithm: CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks
Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <https://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Bryon Aragam [aut, cre], Dacheng Zhang [aut] |
| Maintainer | Bryon Aragam <sparsebn@gmail.com> |
| License | GPL (>= 2) |
| Version | 0.0.6 |
| URL | https://github.com/itsrainingdata/ccdrAlgorithm |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the ccdrAlgorithm package in your browser
Any scripts or data that you put into this service are public.
