fdasrvf

版本 3.6.3 (7.7 MB) 作者: tetonedge
MATLAB library for elastic functional data analysis
5.0
(5)
876.0 次下载
更新时间 2023/12/21
在 GitHub 上查看许可证
MATLAB library for elastic functional data analysis
A MATLAB package for functional data analysis using the square root velocity framework which performs pair-wise and group-wise alignment as well as modeling using functional component analysis
Installation
Download zip or tar.gz of package or clone repository
Run setup.m to setup paths and compile MEX functions NOTE: Armadillo c++ library required for bayesian code.
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).
Xie, W., S. Kurtek, K. Bharath, and Y. Sun (2016). "A Geometric Approach to Visualization of Variability in Functional Data." Journal of the American Statistical Association in press: 1-34.
Y. Lu, R. Herbei and S. Kurtek (2017). "Bayesian Registration of Functions with a Gaussian Process Prior." Journal of Computational and Graphical Statistics: in press: 1-34
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.

引用格式

tetonedge (2024). fdasrvf (https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.6.3), GitHub. 检索时间: .

MATLAB 版本兼容性
创建方式 R2016b
兼容任何版本
平台兼容性
Windows macOS Linux
类别
在 Help Center 和 MATLAB Answers 中查找有关 Call C++ from MATLAB 的更多信息

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

armadillo_cpp/armadillo/mex_interface

basis

basis/@Lfd

basis/@basis

bspline_tools

bspline_tools/@Bspline

bspline_tools/@MscaleBspline

bspline_tools/deprecated

bspline_tools/doc

bspline_tools/tests

minFunc

util

bspline_tools/doc

无法下载基于 GitHub 默认分支的版本

版本 已发布 发行说明
3.6.3

See release notes for this release on GitHub: https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.6.3
3.6.1.0

See release notes for this release on GitHub: https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.6.1
3.6

See release notes for this release on GitHub: https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.6
3.5

See release notes for this release on GitHub: https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.5
3.4

See release notes for this release on GitHub: https://github.com/jdtuck/fdasrvf_MATLAB/releases/tag/3.4
3.0

added new functions from recent advancements in tolerance bounds and principal component regression
2.0.0.0

要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。