Software

Our code is hosted on our lab Github.

For example–

pyUoI: Python package implementing several statistical-machine learning algorithms in the Union of Intersections framework which infers models with accurate feature selection (low false positives and low false negatives) and estimation (low bias and low variance).

github: https://github.com/BouchardLab/pyuoi

papers:

1. PyUoI: The Union of Intersections Framework in Python. Journal of Open Source Software, Sachdeva, P., Livezey, J., Tritt, A.J., Bouchard, K.E. 4(44), 1799, https://doi.org/10.21105/joss.01799. 2019
2. Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. Bouchard, K.E., Bujan, A.F., Roosta, F., Prabhat, Snijders, A., Mao, J-H., Chang, E.F., Mahoney, M., Bhattacharyya, S.; Advances in Neural Information Processing Systems, 2017.
3. Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models. Ruiz, T., Bhattacharyya, S., Balasubramanian, M., Bouchard, K.E., In Learning for Dynamics and Control, 2020.
4. UoI-NMFcluster: A Robust Non-negative Matrix Factorization Algorithm for Improved Parts-Based Decompositions from Noisy Data. Ubaru, S., Wu, K.J., Saad, Y., Bouchard, K.E.; 16th IEEE International Conference on Machine Learning and Applications. DOI: 1109/ICMLA.2017.0-152; Best Paper Award. 2017.
5. Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery.  Sachdeva, P.S., Livezey, J.A., Dougherty, M.E., Gu, D.M., Berke, J.D., Bouchard, K.E., bioRxiv
6. Sparse, Predictive, and Interpretable Functional Connectomics with UoI-Lasso. P.S. Sachdeva, S. Bhattacharyya, Bouchard, K.E.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2019).

 

DCA: Dynamical Components Analysis is an unsupervised dimensionality reduction algorithm that finds low-dimensional subspaces with high dynamical complexity.

github: https://github.com/BouchardLab/DynamicalComponentsAnalysis

papers:

1. Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. Livezey, J, Clark, D.G., Bouchard, K.E.; NeurIPS 2019.

 

NWB: Neurodata Without Borders is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. Our lab bolstered NWB’s advancement through support in developing its software architecture, data ecosystem, and open software strategy. For more information on using NWB, visit https://www.nwb.org/.

github: https://github.com/NeurodataWithoutBorders

papers:

1. The Neurodata Without Borders ecosystem for neurophysiological data science. Rübel,O., Tritt., A.J., …., Bouchard, K.E. eLife.
2. NWB:N 2.0: An Accessible Data Standard for Neurophysiology. Rübel,O., Tritt., A.J., …., Bouchard, K.E. https://www.biorxiv.org/content/10.1101/523035v1
3. HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards. Tritt, A., Rübel, O., Dichter, B., Ly, R., Chang, E., Kang, D., Frank, L., Bouchard, K.E.; IEEE Big Data, 2019.