ggalluvial - Alluvial Plots in 'ggplot2'
Alluvial plots use variable-width ribbons and stacked bar plots to represent multi-dimensional or repeated-measures data with categorical or ordinal variables; see Riehmann, Hanfler, and Froehlich (2005) <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010) <doi:10.1371/journal.pone.0008694>. Alluvial plots are statistical graphics in the sense of Wilkinson (2006) <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and parallel sets plots but are uniquely determined from the data and a small set of parameters. This package extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate alluvial plots from tidy data.
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alluvial-diagramsalluvial-plotscategorical-data-visualizationggplot2repeated-measures-data
15.47 score 520 stars 39 dependents 5.0k scripts 40k downloadsTDAstats - 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>.
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data-scienceggplot2homologyhomology-calculationshomology-computationjosspersistent-homologypipelineripsertdatopological-data-analysistopologytopology-visualizationvisualizationcpp
8.79 score 41 stars 2 dependents 70 scripts 373 downloadsordr - A 'Tidyverse' Extension for Ordinations and Biplots
Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) <doi:10.1093/biomet/58.3.453>, is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'Tidyverse' workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
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biplotdata-visualizationdimension-reductiongeometric-data-analysisgrammar-of-graphicslog-ratio-analysismultivariate-analysismultivariate-statisticsordinationtidymodelstidyverse
7.63 score 27 stars 35 scripts 264 downloadsripserr - Calculate Persistent Homology with Ripser-Based Engines
Ports the Ripser <doi:10.48550/arXiv.1908.02518> and Cubical Ripser <doi:10.48550/arXiv.2005.12692> persistent homology calculation engines from C++. Can be used as a rapid calculation tool in topological data analysis pipelines.
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algebraic-topologycohomologycppcubical-complexpersistent-homologypixelpoint-cloudr-languager-programmingrcpprips-complexripsersimplicial-complexsimplicial-homologytopological-data-analysistopologyvietoris-complexvoxelcpp
7.42 score 12 stars 46 scripts 282 downloadsordered - 'parsnip' Engines and Wrappers for Ordinal Classification Models
Bindings, methods, and tuners for using ordinal classification models with the 'parsnip' and 'dials' packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06> in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) <doi:10.18637/jss.v047.i10> in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) <doi:10.1007/s00357-018-9302-x> in 'ordinalForest'.
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ordinal-classificationordinal-regressionparsniptidymodels
5.70 score 9 stars 40 scripts 570 downloadstdarec - A 'recipes' Extension for Persistent Homology and Its Vectorizations
Topological data analytic methods in machine learning rely on vectorizations of the persistence diagrams that encode persistent homology, as surveyed by Ali &al (2000) <doi:10.48550/arXiv.2212.09703>. Persistent homology can be computed using 'TDA' and 'ripserr' and vectorized using 'TDAvec'. The Tidymodels package collection modularizes machine learning in R for straightforward extensibility; see Kuhn & Silge (2022, ISBN:978-1-4920-9644-3). These 'recipe' steps and 'dials' tuners make efficient algorithms for computing and vectorizing persistence diagrams available for Tidymodels workflows.
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machine-learningpersistent-homologyrecipestidymodelstopological-data-analysisvectorization
4.94 score 1 stars 16 scripts 524 downloadstdaunif - 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>.
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manifoldssamplertdatopological-data-analysistopological-statistics
4.86 score 4 stars 12 scripts 143 downloadsrgph - Pair Critical Points and Compute Persistent Homology of Reeb Graphs
Interface to the 'ReebGraphPairing' program to compute critical points of Reeb graphs following Tu, Hajij, & Rosen (2019) <doi:10.1007/978-3-030-33720-9_8> via the 'rJava' package. Also store Reeb graphs in a minimal S3 class, convert between other network data structures, and post-process pairing data to obtain extended persistent homology following Carrière & Oudot (2018) <doi:10.1007/s10208-017-9370-z>.
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openjdk
2.70 score 6 scripts 225 downloads