论文标题
R中的高维无向图估计的巨大包装
The huge Package for High-dimensional Undirected Graph Estimation in R
论文作者
论文摘要
我们描述了一个名为“巨大”的R软件包，该软件包提供了易于使用的功能，用于从数据中估算高维无向图。该软件包实现了文献中的最新结果，包括Friedman等人。 （2007），刘等。 （2009，2012）和Liu等。 （2010）。与现有的图形估计包Glasso相比，巨大的软件包提供了额外的功能：（1）而不是使用fortan，它是用C编写的，这使代码更便携，更易于修改； （2）除了拟合高斯图形模型外，它还提供了拟合高维半摩托比亚模型模型的功能； （3）更多功能，例如数据依赖性模型选择，数据生成和图形可视化； （4）纠正了图形套索算法的小收敛问题； （5）该软件包允许用户同时应用无损和有损筛查规则来扩展大规模问题，从而在计算效率和统计效率之间进行权衡。
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.
