• robCompositions

  • Referenced in 16 articles [sw11804]
  • principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis ... well as high and low-level plot functions for the ternary diagram...
  • factoextra

  • Referenced in 4 articles [sw18447]
  • Principal Component Analysis), ’CA’ (Correspondence Analysis), ’MCA’ (Multiple Correspondence Analysis), ’FAMD’ (Factor Analysis of Mixed ... Multiple Factor Analysis) and ’HMFA’ (Hierarchical Multiple Factor Analysis) functions from different R packages...
  • MACF

  • Referenced in 1 article [sw40685]
  • auto- and cross-correlation function. Moving dynamic principal component analysis for non-stationary multivariate time ... This paper proposes an extension of principal component analysis to non-stationary multivariate time series ... determining the number of final retained components is proposed. An advance correlation matrix is developed ... evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method...
  • S+ FDA

  • Referenced in 3 articles [sw10814]
  • data analysis (FDA) handles longitudinal data and treats each observation as a function of time ... functions are related. The goal is to analyze a sample of functions instead ... analytic techniques in a number of ways. Functions can be evaluated at any point ... functional data including linear models, generalized linear models, principal components, canonical correlations, principal differential analysis...
  • freqdom

  • Referenced in 3 articles [sw32409]
  • Analysis: Dynamic PCA. Implementation of dynamic principal component analysis (DPCA), simulation ... Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component
  • phytools

  • Referenced in 14 articles [sw10003]
  • from species. For example, the package includes functions for Bayesian and ML ancestral state estimation ... Bayesian posterior sample); conducting an analysis called stochastic character mapping, in which character histories ... response variables; conducting a phylogenetic principal components analysis, a phylogenetic regression, a reduced major axis ... regression, a phylogenetic canonical correlation analysis, and a phylogenetic ANOVA; projecting a tree onto...
  • ANTsR

  • Referenced in 4 articles [sw25746]
  • functionality in ANTsR includes image segmentation and registration along imaging specific variations of principal component ... canonical correlation analysis...
  • GTSPCA

  • Referenced in 1 article [sw38467]
  • GTSPCA: Generalized principal component analysis for non-stationary vector time series. This function is used...
  • SpaSM

  • Referenced in 5 articles [sw23903]
  • performing sparse regression, classification and principal component analysis. The toolbox has been developed ... consists of a series of pure Matlab functions. Examples, test cases and utility functions...
  • Bio3d

  • Referenced in 4 articles [sw15706]
  • mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal ... molecular dynamics data. In addition, various utility functions are provided to enable the statistical...
  • PyClimate

  • Referenced in 3 articles [sw29475]
  • analysis of geophysical fields, such as principal component analysis and related tasks (truncation rules ... analytical and Monte Carlo techniques). Other functions perform singular value decomposition of covariance matrices...
  • bapred

  • Referenced in 0 articles [sw15791]
  • each of these we provide an additional function which enables a posteriori (’addon’) batch effect ... batch effects using principal component analysis. The main functions of the package for batch effect...
  • pyKLIP

  • Referenced in 1 article [sw36382]
  • point spread function (PSF) subtraction. KLIP is based off of principal component analysis to model...
  • AMADA

  • Referenced in 1 article [sw17191]
  • transformation, principal component analysis, a variety of cluster analyses and extensive graphic functions for visualizing...
  • onlinePCA

  • Referenced in 4 articles [sw21315]
  • indispensable to routinely perform tasks like principal component analysis (PCA). Recursive algorithms that update ... data. Extensions to missing data and to functional data are discussed. All studied algorithms...
  • Gmedian

  • Referenced in 2 articles [sw21318]
  • Matrix with Application to Online Robust Principal Components Analysis. The geometric median covariation matrix ... extended without any difficulty to functional data. We define estimators, based on recursive algorithms, that ... weak conditions. The computation of the principal components can also be performed online and this ... data is small and robust principal components analysis based on projection pursuit and spherical projections...
  • TOMCAT

  • Referenced in 3 articles [sw01049]
  • Among the implemented methods there are Principal Component Analysis and its robust variant, Partial Least ... Regression, Robust Continuum Regression and Radial Basis Functions Partial Least Squares...
  • Rfwdmv

  • Referenced in 3 articles [sw25518]
  • search for the analysis of multivariate data. The package provides functions useful for detecting atypical ... Additionally, the package contains functions for performing robust principal component analyses and robust discriminant analyses...
  • MTS

  • Referenced in 7 articles [sw15485]
  • principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs ... structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling ... also performs forecasting using diffusion index, transfer function analysis, Bayesian estimation of VAR models...
  • ChemoSpec

  • Referenced in 1 article [sw15966]
  • functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis...