Software

• vaeST: A Two-Stage Longitudinal Variational Autoencoder for Spatiotemporal Data (Author: Samuel Berchuck)
  • This is supplementary code for the following two manuscripts:
    • Berchuck, S., Mukherjee, S., and Medeiros, F. “Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder”. Scientific Reports (2019).
    • Berchuck, S., Medeiros, F., and Mukherjee, S. “Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma”. Under Revision.

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• spBFA: Spatial Bayesian Factor Analysis (Author: Samuel Berchuck)
  • Berchuck, S., Janko, M., Pan, W., Medeiros, F., and Mukherjee, S. “Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces”. Under Revision.

• spCP: Spatially Varying Change Points (Author: Samuel Berchuck)
  • Berchuck S.I., Mwanza J.C., and Warren J.L. A spatially varying change points model for monitoring glaucoma progression using visual field data, Spatial Statistics (2019).

• womblR: Spatiotemporal Boundary Detection Model for Areal Unit Data (Author: Samuel Berchuck)
  • Berchuck S. I., Mwanza J.C., and Warren J.L. (2018). Diagnosing glaucoma progression with visual field data using a spatiotemporal boundary detection method. Journal of the American Statistical Association (2019).