Software
AnisotropicGrowth: Detect Anisotropy in Fingerprint Growth
Description: Tool chain to statistically detect, with a given confidence, anisotropic growth in fingerprints and its preferred axis based on minutiae patterns.
Type: | R package |
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Published: | 2018-01-16 |
Authors: | K. Markert, K. Krehl, S. Huckemann, and C. Gottschlich |
Maintainer: | Stephan Huckemann |
Download: | AnisotropicGrowth_0.1.3.tar.gz |
Publication: | K. Markert, K. Krehl, C. Gottschlich, and S. Huckemann. Detecting anisotropy in fingerprint growth. J. R. Stat. Soc. C 68 (2019) |
Barycenter: Regularized Wasserstein Distances and Barycenters
Description: Computations of entropy regularized Wasserstein Distances, a.k.a. dual-Sinkhorn divergences, and entropy regularized Wasserstein Barycenters.
Type: | R package |
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Published: | 2018-05-04 |
Author: | M. Klatt |
Maintainer: | Marcel Klatt |
Link: | https://cran.r-project.org/package=Barycenter |
entropicGW: Computing an Entropic Version of the Gromov-Wasserstein Distance
Description: Computing an entropic version of the Gromov-Wasserstein distance.
Type: | R package |
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Published: | 2016-09-08 |
Author: | C. Weitkamp |
Maintainer: | Christoph Weitkamp |
Download: | entropicGW_1.0.tar.gz |
FAST: A Deterministic Sparse FFT for Functions with Structured Fourier Sparsity
Description: A deterministic sparse FFT algorithm (FAST) for functions with block-structured Fourier sparsity.
Type: | C++ code |
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Published: | 2017-05-05 |
Authors: | S. Bittens, R. Zhang, and M.A. Iwen |
Download: | FAST_block_sparse_Cpp.zip |
Publication: | S. Bittens, R. Zhang, and M.A. Iwen. A deterministic sparse FFT for functions with structured Fourier sparsity. Adv. Comput. Math. 45 (2019) |
MiSeal: Minutiae Separating Algorithm
Description: This project includes the MiSeal algorithm for separating a given minutiae pattern into necessary and random minutiae. Moreover, we provide a graphical tool for investigating a selected number of a fingerprint's features such as its orientation field (OF), ridge frequency as well as its OFs divergence field and the intensity of necessary minutiae within patches.
Type: | R package |
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Published: | 2020-09-18 |
Authors: | J. Wieditz, Y. Pokern, D. Schuhmacher, and S. Huckemann |
Maintainer: | Johannes Wieditz |
Link: | https://github.com/jwieditz/MiSeal |
Publication: | J. Wieditz, Y. Pokern, D. Schuhmacher, and S. Huckemann. Characteristic and Necessary Minutiae in Fingerprints, preprint 2020 |
MOP: Multiscale OPtimization
Description: This package implements the Multiscale Nemirovski-Dantzig estimator (MIND) and its related estimators for nanparametric regression and statistical inverse problems.
Type: | MATLAB package |
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Published: | 2015 |
Authors: | H. Li and M. Grasmair |
Link: | https://stochastik.math.uni-goettingen.de/index.php?id=211 |
Publication: | M. Grasmair, H. Li, and A. Munk. Variational multiscale nonparametric regression: Smooth functions. Ann. Inst. H. Poincaré Probab. Statist. 54 (2018) |
MultScVisTransport: Animations of Transport at Different Scales
Description: Takes two input Pgrids and a tranport plan between the two and generates a gif showing the mass transported at a specified range of transport lengths.
Type: | R package |
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Published: | 2018-09-06 |
Author: | F. Heinemann |
Maintainer: | Florian Heinemann |
Download: | MultScVisTransport_0.1.0.tar.gz |
otinference: Inference for Optimal Transport
Description: Sample from the limiting distributions of empirical Wasserstein distances under the null hypothesis and under the alternative. Perform a two-sample test on multivariate data using these limiting distributions and binning.
Type: | R package |
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Published: | 2017-03-07 |
Author: | M. Sommerfeld |
Maintainer: | Max Sommerfeld |
Link: | https://cran.r-project.org/package=otinference |
Publication: | M. Sommerfeld and A. Munk. Inference for empirical Wasserstein distances on finite spaces. J. R. Stat. Soc. B 80 (2018) |
RBEPWT: Region Based Easy Path Wavelet Transform
Description: A Region Based EPWT-like method for Sparse Image Representation.
Type: | Python code |
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Published: | 2017 |
Author: | R. Budinich |
Link: | https://github.com/nareto/rbepwt |
Publication: | R. Budinich. A region-based easy-path wavelet transform for sparse image representation. Int. J. Wavelets Multi. 15 (2017) |
SBB: Spherical Branch And Bound Algorithms
Description: An R Package containing the Spherical Branch and Bound (SBB) algorithm for computing Fréchet means for the circle and the 2-sphere equipped with the arc length as metric. Moreover, a wrapper for computing Fréchet means on more general spaces, provided corresponding branch and bound rules, is provided.
Type: | R package |
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Published: | 2020-11-23 |
Authors: | G. Eichfelder, T. Hotz, and J. Wieditz |
Maintainer: | Johannes Wieditz |
Link: | https://github.com/jwieditz/SphericalBranchAndBound |
Publication: | G. Eichfelder, T. Hotz, and J. Wieditz. An algorithm for computing Fréchet means on the sphere. Optim. Lett. 13 (2019) |
Semi-Discrete Optimal Transport for Unsquared Euclidean Distance
Description: Computation and visualization of the (semi-discrete) optimal transport for the Euclidean cost function from a continuous to a discrete measure.
Type: | C++ code |
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Published: | 2017 |
Authors: | V. Hartmann and D. Schuhmacher |
Link: | https://github.com/valentin-hartmann-research/semi-discrete-transport |
Publication: | V. Hartmann and D. Schuhmacher. Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case. Math. Meth. Oper. Res. (2020) |
SlamR: Finite Alphabet Blind Source Separation in Change-Point Regression
Description: This package implements the estimator SLAM.
Type: | R package |
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Published: | 2018 |
Author: | M. Behr |
Link: | https://stochastik.math.uni-goettingen.de/index.php?id=226 |
Publication: | M. Behr, C. Holmes, and A. Munk. Multiscale blind source separation. Ann. Statist. 46 (2018) |
SuperMPLE: Maximum pseudo-likelihood estimation for superpositions of Strauss-hard core and Poisson processes
Description: This project includes an R package containing an algorithm to compute a maximum pseudo-likelihood estimator for the superposition of a Strauss process (possibly with hard core) and a Poisson process.
Type: | R package |
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Published: | 2021 |
Author: | J. Wieditz |
Maintainer: | Johannes Wieditz |
Link: | https://github.com/jwieditz/SuperMPLE |
Publication: | J. Wieditz. Characteristic and necessary minutiae in fingerprints. Dissertation (2021+) |
transport: Computation of Optimal Transport Plans and Wasserstein Distances
Description: Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet–Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.
Type: | R package |
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Published: | 2020-03-11 |
Authors: | D. Schuhmacher, B. Bähre, N. Bonneel, C. Gottschlich, V. Hartmann, F. Heinemann, B. Schmitzer, J. Schrieber, and T. Wilm |
Maintainer: | Dominic Schuhmacher |
Link: | https://cran.r-project.org/package=transport |
ttbary: Barycenter Methods for Spatial Point Patterns
Description: Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics.
Type: | R package |
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Published: | 2019-12-13 |
Authors: | R. Müller and D. Schuhmacher |
Maintainer: | Dominic Schuhmacher |
Link: | https://cran.r-project.org/package=ttbary |
Publication: | R. Müller, D. Schuhmacher, and J. Mateu. Metrics and barycenters for point pattern data. Stat. Comput. 30 (2020) |
WassersteinPCA: Compute Principal Components in the 2-Wasserstein-Space
Description: This package computes Principal Geodesics and Barycenters in the 2-Wasserstein-Space to perform PCA on images.
Type: | R package |
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Published: | 2018-11-06 |
Author: | F. Heinemann |
Maintainer: | Florian Heinemann |
Download: | WassersteinPCA_1.0.tar.gz |
WSGeometry: Compute Wasserstein Barycenters, Geodesics, PCA and Distances
Description: Includes a variety of methods to compute objects related to the 'Wasserstein distance' (also known as 'Kantorovich distance' or 'Earth-Mover distance'). The main effort of this package is to allow for computations of 'Wasserstein barycenter' using regularised, unregularised and stochastic methods. It also provides convenient wrappers to call the 'transport' package with more general inputs. Handy visual tools are provided to showcase, barycenters, animations of optimal transport geodesics and animations of principal components in the 'Wasserstein space'.
Type: | R package |
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Published: | 2021-01-05 |
Authors: | F. Heinemann and N. Bonneel |
Maintainer: | Florian Heinemann |
Link: | https://cran.r-project.org/package=WSGeometry |
Publication: | F. Heinemann, A. Munk, and Y. Zemel. Randomised Wasserstein Barycenter Computation: Resampling with Statistical Guarantees, preprint 2020 |