# An Interface to the GUDHI Library for Topological Data Analysis

## Documentation for package ‘rgudhi’ version 0.2.0

- DESCRIPTION file.

## Help Pages

AffinityPropagation | Performs clustering according to the affinity propagation algorithm
AgglomerativeClustering | Performs clustering according to the agglomerative algorithm
AlphaComplex | R6 Class for Alpha Complex
as_persistence_diagram | Persistence Diagram
as_persistence_diagram_sample | Persistence Diagram Sample
Atol | Vector Representation: Atol
autoplot.persistence_diagram | Plot for 'persistence_diagram' objects
BettiCurve | Vector Representation: Betti Curve
Birch | Performs clustering according to the Birch algorithm
BirthPersistenceTransform | Preprocessing: Birth Persistence Transform
BisectingKMeans | Performs clustering according to the bisecting k-means algorithm
BottleneckDistance | Metrics: Bottleneck Distance
clear_data_home | Remote Data Sets
ComplexPolynomial | Vector Representation: Complex Polynomial
CubicalComplex | R6 Class for Cubical Complex
DBSCAN | Performs clustering according to the DBSCAN algorithm
DiagramScaler | Preprocessing: Diagram Scaler
DiagramSelector | Preprocessing: Diagram Selector
Entropy | Vector Representation: Entropy
FeatureAgglomeration | Performs clustering according to the feature agglomeration algorithm
fetch | Remote Data Sets
fetch_bunny | Remote Data Sets
fetch_spiral_2d | Remote Data Sets
is_persistence_diagram | Persistence Diagram
is_persistence_diagram_sample | Persistence Diagram Sample
KMeans | Performs clustering according to the k-means algorithm
Landscape | Vector Representation: Landscape
MaxAbsScaler | Scales each feature by its maximum absolute value
mean.persistence_diagram_sample | Persistence Diagram Sample Mean
MeanShift | Performs clustering according to the mean shift algorithm
MiniBatchKMeans | Performs clustering according to the mini-batch k-means algorithm
MinMaxScaler | Transforms features by scaling each feature to a given range
OPTICS | Performs clustering according to the OPTICS algorithm
Padding | Preprocessing: Padding
PeriodicCubicalComplex | R6 Class for Periodic Cubical Complex
PersistenceFisherDistance | Metrics: Persistence Fisher Distance
PersistenceFisherKernel | Kernel Representation: Persistence Fisher Kernel
PersistenceImage | Vector Representation: Persistence Image
PersistenceScaleSpaceKernel | Kernel Representation: Persistence Scale-Space Kernel
PersistenceSlicedWassersteinKernel | Kernel Representation: Persistence Sliced Wasserstein Kernel
PersistenceWeightedGaussianKernel | Kernel Representation: Persistence Weighted Gaussian Kernel
persistence_diagram | Persistence Diagram
persistence_diagram_sample | Persistence Diagram Sample
plot.persistence_diagram | Plot for 'persistence_diagram' objects
ProminentPoints | Preprocessing: Prominent Points
RipsComplex | R6 Class for Rips Complex
RobustScaler | Scales features using statistics that are robust to outliers
seq_circle | Circular Sequence Generation
Silhouette | Vector Representation: Silhouette
SimplexTree | R6 Class for Simplex Tree
SlicedWassersteinDistance | Metrics: Sliced Wasserstein Distance
SpectralBiclustering | Performs clustering according to the spectral biclustering algorithm
SpectralClustering | Performs clustering according to the spectral clustering algorithm
SpectralCoclustering | Performs clustering according to the spectral coclustering algorithm
sphere | Sampling on the Sphere
StandardScaler | Standardizes features by removing the mean and scaling to unit variance
StrongWitnessComplex | R6 Class for Strong Witness Complex
TangentialComplex | R6 Class for Tangential Complex
Tomato | Clustering: Tomato
TopologicalVector | Vector Representation: Topological Vector
torus | Sampling on the Torus
WassersteinDistance | Metrics: Wasserstein Distance
WitnessComplex | R6 Class for Witness Complex