Package: QuadratiK 1.1.2

Giovanni Saraceno

QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances

It includes test for multivariate normality, test for uniformity on the d-dimensional Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson kernel-based density and clustering algorithm for spherical data. For more information see Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024) <doi:10.48550/arXiv.2402.02290> Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.

Authors:Giovanni Saraceno [aut, cre], Marianthi Markatou [aut], Raktim Mukhopadhyay [aut], Mojgan Golzy [aut]

QuadratiK_1.1.2.tar.gz
QuadratiK_1.1.2.tar.gz(r-4.5-noble)QuadratiK_1.1.2.tar.gz(r-4.4-noble)
QuadratiK_1.1.2.tgz(r-4.4-emscripten)QuadratiK_1.1.2.tgz(r-4.3-emscripten)
QuadratiK.pdf |QuadratiK.html✨
QuadratiK/json (API)
NEWS

# Install 'QuadratiK' in R:
install.packages('QuadratiK', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/giovsaraceno/quadratik-package/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

4.38 score 20 scripts 141 downloads 13 exports 82 dependencies

Last updated 2 days agofrom:01c45f1ccf. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-linux-x86_64OKOct 30 2024

Exports:dpkbkb.testpk.testpkbcpkbc_validationplotpredictrpkbsample_hypersphereselect_hshowstats_clusterssummary

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11DEoptimRDerivdoBydoParalleldplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamnormtmodelrmomentsmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpcaPPpillarpkgconfigpolynompurrrquantregR6RColorBrewerRcppRcppEigenrlangrlecuyerrobustbaserrcovrstatixscalesscatterplot3dsnSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Clustering algorithm on the Wireless data

Rendered fromwireless_clustering.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-02-24

Introduction to the QuadratiK Package

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-10-29

k-sample test

Rendered fromkSample_test.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-02-24

Non-parametric Two-sample test

Rendered fromTwoSample_test.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-02-24

Random sampling from the Poisson kernel-based density

Rendered fromgenerate_rpkb.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-05-15

Uniformity test on the Sphere

Rendered fromuniformity.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-10-29
Started: 2024-02-24

Readme and manuals

Help Manual

Help pageTopics
Collection of Methods Constructed using the Kernel-Based Quadratic DistancesQuadratiK-package QuadratiK
Breast Cancer Wisconsin (Diagnostic)breast_cancer
The Poisson kernel-based Distribution (PKBD)dpkb rpkb
Kernel-based quadratic distance (KBQD) Goodness-of-Fit testskb.test kb.test,ANY-method show,kb.test-method
An S4 class for kernel-based distance tests with normal kernelkb.test-class
Poisson kernel-based quadratic distance test of Uniformity on the spherepk.test pk.test,ANY-method show,pk.test-method
An S4 class for Poisson kernel-based quadratic distance tests.pk.test-class
Poisson kernel-based clustering on the spherepkbc pkbc,ANY-method show,pkbc-method
Validation of Poisson kernel-based clustering resultspkbc_validation
A S4 class for the clustering algorithm on the sphere based on Poisson kernel-based distributions.pkbc-class
Plotting method for Poisson kernel-based clusteringplot,pkbc,ANY-method plot.pkbc
Cluster spherical observations using a mixture of Poisson kernel-based densitiespredict,pkbc-method predict.pkbc
Generate random sample from the hyperspheresample_hypersphere
Select the value of the kernel tuning parameterselect_h
Descriptive statistics for the clusters identified by the Poisson kernel-based clustering.stats_clusters stats_clusters,pkbc-method
Summarizing kernel-based quadratic distance resultssummary,kb.test-method summary.kb.test
Summarizing kernel-based quadratic distance resultssummary,pk.test-method summary.pk.test
Summarizing PKBD mixture Fitssummary,pkbc-method summary.pkbc
Wine data setwine
Wireless Indoor Localizationwireless