# loggle v1.0

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## Local Group Graphical Lasso Estimation

Provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. The underlying statistical model 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 study of how stocks interact with each other and how such interactions change over time. The time-varying graphical models are estimated under the assumption that the graph topology changes gradually over time. For more details on estimating time-varying graphical models, please refer to: Yang, J. & Peng, J. (2018) <arXiv:1804.03811>.

## Readme

# LOGGLE (LOcal Group Graphical Lasso Estimation)

## Description

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`

.

For more details on estimating time-varying graphical models and the package, please refer to: **Estimating Time-Varying Graphical Models** https://arxiv.org/abs/1804.03811.

## Dependencies

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"))
```

## Installation

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")
```

## Main functions

`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 from`loggle.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.

## Contact

Please report any bugs to jlyang@ucdavis.edu.

## Functions in loggle

Name | Description | |

loggle.refit | A function to conduct model refitting given learned graph structures | |

example_source | Source code for generating time-varying graphs | |

loggle | A function to learn time-varying graphical models | |

loggle.cv | A function to learn time-varying graphical models via cross validation | |

loggle.cv.h | A function to learn time-varying graphical models via cross validation (with h fixed) | |

loggle.cv.select | A function to conduct model selection based on cross validation results | |

loggle.cv.vote | A function to learn time-varying graphical models using cv.vote | |

loggle.cv.select.h | A function to conduct model selection based on cross validation results (with h fixed) | |

stockdata_source | Source code for stock prices of S&P 500 companies from 2007 to 2016 | |

stockdata | Stock prices of S&P 500 companies from 2007 to 2016 | |

example | An example dataset of time-varying graphs | |

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## Last month downloads

## Details

Date | 2018-04-11 |

License | GPL (>= 2) |

NeedsCompilation | yes |

URL | https://github.com/jlyang1990/loggle |

Packaged | 2018-04-13 21:22:17 UTC; jlyang |

Repository | CRAN |

Date/Publication | 2018-04-16 09:16:59 UTC |

imports | doParallel (>= 1.0.8) , foreach (>= 1.2.0) , glasso (>= 1.8) , igraph (>= 0.7) , Matrix (>= 1.2) , sm |

suggests | matrixcalc , quantmod , RCurl , sparseMVN , XML |

depends | R (>= 3.0.2) |

Contributors | Jie Peng, Jilei Yang |

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