The R package loggle provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. loggle is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance, the gene regulatory networks over the course of organismal development, and the dynamic relationships between individuals in a community over a few years. loggle estimates time-varying graphical models under the assumption that the graph topology changes gradually over time.
loggle has been applied to S&P 500 stock price dataset, where the interacting relationships among stocks and among industrial sectors in a time period that covers the recent global financial crisis can be revealed. Detailed description of S&P 500 stock price dataset is in ?stockdata.
loggle is available on CRAN now. For more details on estimating time-varying graphical models and the package, please refer to: Yang, J. & Peng, J. (2018), Estimating Time-Varying Graphical Models, arXiv:1804.03811. For codes and data used in the simulation and real data application in this paper, please refer to: https://github.com/jlyang1990/loggle_test.
Please make sure to install the following package dependencies before using R package loggle. R with version later than 3.0.2 is needed.
install.packages(c("Matrix", "doParallel", "igraph", "glasso", "sm"))The R package loggle can be installed from source files in the GitHub repository (R package devtools is needed):
library(devtools)
install_github(repo="jlyang1990/loggle")loggle: learn time-varying graphical models for a given set of tuning parameters.loggle.cv: conduct model selection via cross validation for learning time-varying graphical models.loggle.cv.select: conduct model selection for time-varying graphical models based on cross validation results fromloggle.cv.loggle.cv.vote: learn time-varying graphical models for a given set of tuning parameters via cv.vote.loggle.refit: conduct model refitting given learned time-varying graph structures.
Please report any bugs to jlyang@ucdavis.edu.