Metadata-Version: 1.1
Name: fdasrsf
Version: 2.1.1
Summary: functional data analysis using the square root slope framework
Home-page: http://research.tetonedge.net
Author: J. Derek Tucker
Author-email: jdtuck@sandia.gov
License: LICENSE.txt
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        fdasrsf
        =======
        
        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.
        
        ### Installation
        ------------------------------------------------------------------------------
        v2.1.1 is on pip and can be installed using
        > `pip install fdasrsf`
        
        To install the most up to date version on github
        > `python setup.py install`
        
        ------------------------------------------------------------------------------
        
        ### References
        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.
        
Keywords: functional data analysis
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires: Cython
Requires: matplotlib
Requires: numpy
Requires: scipy
Requires: joblib
Requires: patsy
Requires: tqdm
