Showing 13 of total 13 results (show query)
jkim82133
TDA:Statistical Tools for Topological Data Analysis
Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8> for analyzing the statistical significance of persistent homology features.
Maintained by Jisu Kim. Last updated 1 months ago.
58.8 match 9 stars 7.18 score 204 scripts 5 dependents
rrrlw
TDAstats:Pipeline for Topological Data Analysis
A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.
Maintained by Raoul Wadhwa. Last updated 3 years ago.
data-scienceggplot2homologyhomology-calculationshomology-computationjosspersistent-homologypipelineripsertdatopological-data-analysistopologytopology-visualizationvisualizationcpp
11.0 match 40 stars 8.30 score 46 scripts 4 dependents
tdaverse
tdaunif:Uniform Manifold Samplers for Topological Data Analysis
Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
Maintained by Jason Cory Brunson. Last updated 9 months ago.
manifoldssamplertdatopological-data-analysistopological-statistics
11.5 match 3 stars 4.95 score 8 scripts
traminer
TraMineR:Trajectory Miner: a Sequence Analysis Toolkit
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Maintained by Gilbert Ritschard. Last updated 3 months ago.
3.8 match 11 stars 8.24 score 534 scripts 13 dependents
bioc
PIUMA:Phenotypes Identification Using Mapper from topological data Analysis
The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data.
Maintained by Mattia Chiesa. Last updated 5 months ago.
clusteringgraphandnetworkdimensionreductionnetworkclassification
4.8 match 4 stars 5.08 score 2 scripts
shaelebrown
TDApplied:Machine Learning and Inference for Topological Data Analysis
Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.
Maintained by Shael Brown. Last updated 5 months ago.
2.9 match 16 stars 6.60 score 8 scripts
uiowa-applied-topology
mappeR:Construct and Visualize TDA Mapper Graphs
Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.
Maintained by George Clare Kennedy. Last updated 24 days ago.
3.5 match 2 stars 4.05 score 14 scripts
cran
transDA:Transformation Discriminant Analysis
Performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.
Maintained by Jing Li. Last updated 4 months ago.
12.1 match 1.00 scorecran
RPointCloud:Visualizing Topological Loops and Voids
Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the 'TDA' package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) <doi:10.1101/2024.05.16.593927>.
Maintained by Kevin R. Coombes. Last updated 7 months ago.
4.1 match 2.78 score
miriamesteve
GSSTDA:Progression Analysis of Disease with Survival using Topological Data Analysis
Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.
Maintained by Miriam Esteve. Last updated 8 months ago.
1.1 match 2 stars 5.15 score 7 scriptsbioc
TargetDecoy:Diagnostic Plots to Evaluate the Target Decoy Approach
A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method.
Maintained by Elke Debrie. Last updated 5 months ago.
massspectrometryproteomicsqualitycontrolsoftwarevisualizationbioconductormass-spectrometry
0.9 match 1 stars 4.60 score 9 scripts
dnychka
RadioSonde:Tools for Plotting Skew-T Diagrams and Wind Profiles
A collection of programs for plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument). The format of this plot with companion lines to assess atmospheric stability are both standard in meteorology and difficult to create from basic graphics functions. Hence this package. One novel feature is being able add several profiles to the same plot for comparison. Use "help(ExampleSonde)" for an explanation of the variables needed and how they should be named in a data frame. See <https://github.com/dnychka/Radiosonde> for the package home page.
Maintained by Doug Nychka. Last updated 3 years ago.
3.3 match 1.15 score 14 scripts