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>.
| Version: | 1.0 |
| Depends: | R (≥ 3.0.2) |
| Imports: | Matrix (≥ 1.2), doParallel (≥ 1.0.8), foreach (≥ 1.2.0), igraph (≥ 0.7), glasso (≥ 1.8), sm |
| Suggests: | sparseMVN, matrixcalc, XML, RCurl, quantmod |
| Published: | 2018-04-16 |
| Author: | Jilei Yang, Jie Peng |
| Maintainer: | Jilei Yang <jlyang at ucdavis.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/jlyang1990/loggle |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | loggle results |
| Reference manual: | loggle.pdf |
| Package source: | loggle_1.0.tar.gz |
| Windows binaries: | r-devel: loggle_1.0.zip, r-release: loggle_1.0.zip, r-oldrel: loggle_1.0.zip |
| OS X binaries: | r-release: loggle_1.0.tgz, r-oldrel: loggle_1.0.tgz |
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