Welcome to fdasrsf’s documentation!#

A python package for functional data analysis using the square root slope framework and curves using the square root velocity framework which performs pair-wise and group-wise alignment as well as modeling using functional component analysis and regression.


Currently, fdasrsf is available in Python versions above 3.8, regardless of the platform. The stable version can be installed via PyPI:

pip install fdasrsf

It is also available from conda-forge:

conda install -c conda-forge fdasrsf

It is possible to install the latest version of the package, available in the develop branch, by cloning this repository and doing a manual installation.

git clone https://github.com/jdtuck/fdasrsf_python.git
pip install ./fdasrsf_python

In this type of installation make sure that your default Python version is currently supported, or change the python and pip commands by specifying a version, such as python3.8.

How do I start?#

If you want a quick overview of the package, we recommend you to look at the example notebooks in the Users Guide


All contributions are welcome. You can help this project grow in multiple ways, from creating an issue, reporting an improvement or a bug, to doing a repository fork and creating a pull request to the development branch.


The package is licensed under the BSD 3-Clause License. A copy of the license can be found along with the code or in the project page.


Tucker, J. D. 2014, Functional Component Analysis and Regression using Elastic Methods. Ph.D. Thesis, Florida State University.

Robinson, D. T. 2012, Function Data Analysis and Partial Shape Matching in the Square Root Velocity Framework. Ph.D. Thesis, Florida State University.

Huang, W. 2014, Optimization Algorithms on Riemannian Manifolds with Applications. Ph.D. Thesis, Florida State University.

Srivastava, A., Wu, W., Kurtek, S., Klassen, E. and Marron, J. S. (2011). Registration of Functional Data Using Fisher-Rao Metric. arXiv:1103.3817v2 [math.ST].

Tucker, J. D., Wu, W. and Srivastava, A. (2013). Generative models for functional data using phase and amplitude separation. Computational Statistics and Data Analysis 61, 50-66.

J. D. Tucker, W. Wu, and A. Srivastava, “Phase-Amplitude Separation of Proteomics Data Using Extended Fisher-Rao Metric,” Electronic Journal of Statistics, Vol 8, no. 2. pp 1724-1733, 2014.

J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of signals under compositional noise With applications to SONAR data,” IEEE Journal of Oceanic Engineering, Vol 29, no. 2. pp 318-330, Apr 2014.

Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape analysis of elastic curves in euclidean spaces. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33 (7), 1415-1428.

S. Kurtek, A. Srivastava, and W. Wu. Signal estimation under random time-warpings and nonlinear signal alignment. In Proceedings of Neural Information Processing Systems (NIPS), 2011.

Wen Huang, Kyle A. Gallivan, Anuj Srivastava, Pierre-Antoine Absil. “Riemannian Optimization for Elastic Shape Analysis”, Short version, The 21st International Symposium on Mathematical Theory of Networks and Systems (MTNS 2014).

Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian registration of functions and curves. Bayesian Analysis, 11(2), 447-475.

W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach to visualization of variability in functional data, Journal of American Statistical Association 112 (2017), pp. 979-993.

Lu, Y., R. Herbei, and S. Kurtek, 2017: Bayesian registration of functions with a Gaussian process prior. Journal of Computational and Graphical Statistics, 26, no. 4, 894–904.

Lee, S. and S. Jung, 2017: Combined analysis of amplitude and phase variations in functional data. arXiv:1603.01775 [stat.ME], 1–21.

J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, vol. 12, no. 2, pp. 101-115, 2019.

J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.

T. Harris, J. D. Tucker, B. Li, and L. Shand, “Elastic depths for detecting shape anomalies in functional data,” Technometrics, 10.1080/00401706.2020.1811156, 2020.

M. K. Ahn, J. D. Tucker, W. Wu, and A. Srivastava. “Regression Models Using Shapes of Functions as Predictors” Computational Statistics and Data Analysis, 10.1016/j.csda.2020.107017, 2020.

J. D. Tucker, L. Shand, and K. Chowdhary. “Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo”, Computational Statistics and Data Analysis, accepted, 2021.

X. Zhang, S. Kurtek, O. Chkrebtii, and J. D. Tucker, “Elastic k-means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics”, arXiv:2011.12397 [stat.ME], 2020.

  1. Xie, S. Kurtek, E. Klassen, G. E. Christensen and A. Srivastava. Metric-based pairwise and multiple image registration. IEEE European Conference on Computer Vision (ECCV), September, 2014

    1. Tucker and D. Yarger, “Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources”, Envirometrics, 10.1002/env.2826, 2023.

Indices and tables#