Showing 8 of total 8 results (show query)
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
40 stars 8.30 score 46 scripts 4 dependents
sonsoleslp
tna:Transition Network Analysis (TNA)
Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.
Maintained by Sonsoles López-Pernas. Last updated 3 days ago.
educational-data-mininglearning-analyticsmarkov-modeltemporal-analysis
4 stars 6.51 score 5 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.
16 stars 6.51 score 8 scripts
bioc
structToolbox:Data processing & analysis tools for Metabolomics and other omics
An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.
Maintained by Gavin Rhys Lloyd. Last updated 1 months ago.
workflowstepmetabolomicsbioconductor-packagedimslc-msmachine-learningmultivariate-analysisstatisticsunivariate
10 stars 6.26 score 12 scriptsbioc
dce:Pathway Enrichment Based on Differential Causal Effects
Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
Maintained by Kim Philipp Jablonski. Last updated 3 months ago.
softwarestatisticalmethodgraphandnetworkregressiongeneexpressiondifferentialexpressionnetworkenrichmentnetworkkeggbioconductorcausality
13 stars 4.59 score 4 scripts
nzilbb
nzilbb.vowels:Vowel Covariation Tools
Tools to support research on vowel covariation. Methods are provided to support Principal Component Analysis workflows (as in Brand et al. (2021) <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) <doi:10.1515/lingvan-2022-0086>).
Maintained by Joshua Wilson Black. Last updated 3 months ago.
3.88 score 15 scripts
soroushmdg
gwid:Genome-Wide Identity-by-Descent
Methods and tools for the analysis of Genome Wide Identity-by-Descent ('gwid') mapping data, focusing on testing whether there is a higher occurrence of Identity-By-Descent (IBD) segments around potential causal variants in cases compared to controls, which is crucial for identifying rare variants. To enhance its analytical power, 'gwid' incorporates a Sliding Window Approach, allowing for the detection and analysis of signals from multiple Single Nucleotide Polymorphisms (SNPs).
Maintained by Soroush Mahmoudiandehkordi. Last updated 7 months ago.
1 stars 3.60 score 4 scripts
cran
permutest:Run Permutation Tests and Construct Associated Confidence Intervals
Implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) <doi:10.48550/arXiv.2405.05238>.
Maintained by Amanda Glazer. Last updated 6 months ago.
1.70 score