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A regression technique for modeling temporally diffuse effects
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Continuous-Time Deconvolutional Regression (CDR)

CDR (formerly deconvolutional time series regression or DTSR) is a regression technique for modeling temporally diffuse effects (Shain & Schuler, 2018, 2019).

This repository contains source code for the cdr Python module as well as support for reproducing published experiments. Full documentation for the cdr module is available at

CDR models can be trained and evaluated using provided utility executables. Help strings for all available utilities can be viewed by running python -m Full repository documentation, including an API, is provided at the link above.

Note that some published experiments below also involve fitting LME and GAM models, which require rpy2 and therefore won't work on Windows systems without some serious hacking. The cdr module is cross-platform and therefore CDR models should train regardless of operating system.

Reproducing published results

This repository is under active development, and reproducibility of previously published results is not guaranteed from the master branch. For this reason, repository states associated with previous results are saved in Git branches. To reproduce those results, checkout the relevant branch and follow the instructions in the README. Current reproduction branches are:

  • emnlp18
  • naacl19

Thus, to reproduce results from NAACL19, for example, run git checkout naacl19 from the repository root, and follow instructions in the README file. The reproduction branches are also useful sources of example configuration files to use as templates for setting up your own experiments, although you should consult the docs for full documentation of the structure of CDR experiment configurations.

Published results depend on both (1) datasets and (2) models as defined in experiment-specific configuration files. In general, we do not distribute data with this repository. The datasets used can be provided by email upon request.

Help and support

For questions, concerns, or data requests, contact Cory Shain ( Bug reports can be logged in the issue tracker on Github.


Shain, Cory and Schuler, William (2018). Deconvolutional time series regression: A technique for modeling temporally diffuse effects. EMNLP18. Shain, Cory and Schuler, William (2019). Continuous-time deconvolutional regression for psycholinguistic modeling. PsyArXiv.

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