• t-SNE

  • Referenced in 66 articles [sw22300]
  • technique called ”t-SNE” that visualizes high-dimensional data by giving each datapoint a location ... scales. This is particularly important for high-dimensional data that lie on several different...
  • rda

  • Referenced in 70 articles [sw06091]
  • Analysis for the classification purpose in high dimensional data...
  • ROBPCA

  • Referenced in 54 articles [sw11592]
  • empirical covariance matrix of the data and hence is highly sensitive to outlying observations ... limited to relatively low-dimensional data. The second approach is based ... projection pursuit and can handle high-dimensional data. Here we propose the ROBPCA approach, which...
  • mboost

  • Referenced in 50 articles [sw07331]
  • additive and interaction models to potentially high-dimensional data...
  • GGobi

  • Referenced in 40 articles [sw00345]
  • open source visualization program for exploring high-dimensional data. It provides highly dynamic and interactive...
  • CoCoA

  • Referenced in 585 articles [sw00143]
  • operations on multivaraiate polynomials and on various data related to them (ideals, modules, matrices, rational ... ideal, the ideal of zero-dimensional schemes, Poincare’ series and Hilbert functions, factorization of polynomials ... further enhanced by the dedicated high-level programming language. For convenience, the system offers...
  • LAS

  • Referenced in 16 articles [sw17180]
  • Finding large average submatrices in high dimensional data. The search for sample-variable associations ... problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable ... form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix ... discovery of biologically relevant structures in high dimensional data. Software is available...
  • FAMT

  • Referenced in 25 articles [sw11123]
  • FAMT) : simultaneous tests under dependence in high-dimensional data. The method proposed in this package ... dependence on the multiple testing procedures for high-throughput data as proposed by Friguet...
  • HDclassif

  • Referenced in 14 articles [sw11114]
  • analysis and data clustering methods for high dimensional data, based on the assumption that high...
  • mixOmics

  • Referenced in 19 articles [sw09508]
  • integrative techniques and variants to analyse highly dimensional data sets: regularized CCA and sparse ... samples or individuals n. These data may come from high throughput technologies, such as omics...
  • huge

  • Referenced in 30 articles [sw08466]
  • functions for estimating high dimensional undirected graphs from data. This package implements recent results ... fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection...
  • GAP

  • Referenced in 15 articles [sw26294]
  • matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception...
  • ARfit

  • Referenced in 33 articles [sw00046]
  • efficient, in particular when the data are high-dimensional. ARfit modules construct approximate confidence intervals...
  • WGCNA

  • Referenced in 15 articles [sw07123]
  • perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning...
  • funHDDC

  • Referenced in 14 articles [sw11130]
  • data which adapts the clustering method high dimensional data clustering (HDDC), originally proposed...
  • SOM_PAK

  • Referenced in 23 articles [sw15446]
  • codebook vectors into a high-dimensional input data space to approximate to its data sets...
  • ranger

  • Referenced in 10 articles [sw14498]
  • Fast Implementation of Random Forests for High Dimensional Data in C++ and R. We introduce ... fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival...
  • uniCox

  • Referenced in 12 articles [sw19131]
  • model.. Especially useful for high-dimensional data, including microarray data...
  • Isomap

  • Referenced in 11 articles [sw31686]
  • Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar ... problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations ... everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers ... Here we describe an approach to solving dimensionality reduction problems that uses easily measured local...
  • hgam

  • Referenced in 73 articles [sw11201]
  • sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness ... well as performance for finite-sample data. We present a computationally efficient algorithm, with provable ... asymptotic optimality of our estimator for high dimensional but sparse additive models. Finally, an adaptive...