Warning
I worked on this code base during my master's project in 2021 and today it is no longer actively maintained. Since the TensorFlow interface uses reticulate, you may run into compatibility issues with Python dependencies (see #4). While these things should be salvageable and I welcome contributions through pull requests, I have no capacity to work on this package myself.
Note
This is the repository for the companion package to our paper @altmeyer2021deep. We recently presented our paper at the NeurIPS 2021 MLECON workshop. For the specific code used for the paper and additional resources please see this repository.
The deepvars package provides a framework for Deep Vector
Autoregression in R. The methodology is based on (Altmeyer, Agusti, and
Vidal-Quadras Costa 2021), a working paper initially prepared as part of
the Masters Degree in Data
Science
at Barcelona School of Economics. For a summary of the
first version of the working paper see
here.
As one of its dependencies the deepvars uses tensorflow, which is an
R interface to the popular TensorFlow
library. We have tried to automate the TensorFlow configuration as
explained
here.
install.packages("tensorflow")
tensorflow::install_tensorflow()For uncertainty quantification we use tensorflow_probability for
Bayesian inference.
install.packages("tfprobability")
tfprobability::install_tfprobability()Should you run into issues you may have to manually install the TensorFlow dependencies. Detailed instructions to this end can be found here.
You can either clone this repository and install from source or simply run the below in R:
devtools::install_github("pat-alt/deepvars", build_vignettes=TRUE)
library(deepvars)Full documentation of the package is still a work-in-progress. In the
meantime, detailed guidance on different topics and estimation methods
covered by deepvars, can be found in the vignettes. Simply type the
following command once you have completed the steps above:
utils::browseVignettes('deepvars')