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pvclust

R package pvclust: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling. pvclust is a package for assessing the uncertainty in hierarchical cluster analysis. It provides AU (approximately unbiased) p-values as well as BP (boostrap probability) values computed via multiscale bootstrap resampling.

Keywords for this software

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  • clustering
  • classification
  • statistical significance
  • microarray gene expression data
  • Journal of Statistical Software
  • scaling-law
  • R package
  • low-sample size data
  • (p) value
  • Edgeworth expansion
  • bias correction and acceleration
  • co-correspondence analysis
  • mean curvature
  • fourth-order accuracy
  • networks
  • iterated bootstrap
  • variable ranking
  • big data
  • semi-supervised learning
  • hypothesis test
  • significance of model selection
  • retailing
  • approximately unbiased tests
  • number of clusters
  • bootstrap resampling
  • demand learning
  • principal response curves
  • homogeneity test
  • second-order unbiased (p)-value
  • correspondence analysis

  • URL: cran.r-project.org/web...
  • Code
  • InternetArchive
  • Manual: cran.r-project.org/web...
  • Authors: Ryota Suzuki, Hidetoshi Shimodaira
  • Dependencies: R

  • Add information on this software.


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References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
y Sorted by year (citations)

  1. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  2. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  3. Cybis, Gabriela B.; Valk, Marcio; Lopes, Sílvia R. C.: Clustering and classification problems in genetics through (U)-statistics (2018)
  4. Lu, Qiyi; Qiao, Xingye: Significance analysis of high-dimensional, low-sample size partially labeled data (2016)
  5. Gallien, Jérémie; Mersereau, Adam J.; Garro, Andres; Mora, Alberte Dapena; Vidal, Martín Nóvoa: Initial shipment decisions for new products at Zara (2015)
  6. Malika Charrad; Nadia Ghazzali; Véronique Boiteau; Azam Niknafs: NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014) not zbMATH
  7. Shimodaira, Hidetoshi: Higher-order accuracy of multiscale-double bootstrap for testing regions (2014)
  8. Ueki, M.; Fueda, K.: A bias correction and acceleration approach for the problem of regions (2009)
  9. Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J. S.: Statistical significance of clustering for high-dimension, low-sample size data (2008)

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      • 62 Statistics
      • 68 Computer science
      • 90 Optimization
      • 92 Applications of...

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