
refund
 Referenced in 33 articles
[sw07434]
 functional data using principal components. Functional principal components (FPC) analysis is widely used to decompose ... observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions ... constructed. Standard mixed model representations of functional expansions are used to construct curve estimates ... implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably...

CGAL
 Referenced in 319 articles
[sw00118]
 spheres, smallest enclosing ellipsoid of points, principal component analysis), and kinetic data structures. All these ... offers geometric object generators and spatial sorting functions, as well as a matrix search framework...

SuLQ
 Referenced in 118 articles
[sw11355]
 First, we modify the privacy analysis to realvalued functions f and arbitrary row types ... very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm...

fpca
 Referenced in 4 articles
[sw21039]
 package fpca: Restricted MLE for Functional Principal Components Analysis. A geometric approach...

LIBRA
 Referenced in 25 articles
[sw10553]
 LIBRA. LIBRA: a MATLAB Library for Robust Analysis is developed at ROBUST@Leuven, the research ... data. Currently, the library contains functions for univariate location, scale and skewness, multivariate location ... estimation (MCD), regression (LTS, MCDregression), Principal Component Analysis (RAPCA, ROBPCA), Principal Component Regression (RPCR ... quantiles. For comparison also several nonrobust functions are included. Many graphical tools are provided...

rsvd
 Referenced in 5 articles
[sw16104]
 widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized ... also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several...

PACE
 Referenced in 2 articles
[sw24632]
 program of this package is Functional Principal Component Analysis (FPCA), a key technique for functional ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm...

sparseFLMM
 Referenced in 2 articles
[sw21042]
 sparsely sampled data based on functional principal component analysis...

MFPCA
 Referenced in 2 articles
[sw16091]
 package MFPCA. Calculate a multivariate functional principal component analysis for data observed on different dimensional ... expansions for each element of the multivariate functional data. Multivariate and univariate functional data objects...

funData
 Referenced in 2 articles
[sw21036]
 oriented data analysis, it is shown why it is natural to implement functional data ... MFPCA package, which implements multivariate functional principal component analysis, is presented as an example...

fdapace
 Referenced in 1 article
[sw15968]
 core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm...

PACE
 Referenced in 1 article
[sw28690]
 core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. PACE...

amap
 Referenced in 2 articles
[sw19769]
 Clustering and Principal Component Analysis (With robust methods, and parallelized functions...

pcdpca
 Referenced in 1 article
[sw26261]
 Series. Method extends multivariate and functional dynamic principal components to periodically correlated multivariate time series ... allows you to compute true dynamic principal components in the presence of periodicity. We follow ... Jouzdani (2017), in Principal component analysis of periodically correlated functional time series...

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...

robCompositions
 Referenced in 7 articles
[sw11804]
 principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis ... well as high and lowlevel plot functions for the ternary diagram...

LowRankModels
 Referenced in 12 articles
[sw27002]
 models in data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative ... easy to mix and match loss functions and regularizers to construct a model suitable...

phytools
 Referenced in 11 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 3 articles
[sw25746]
 functionality in ANTsR includes image segmentation and registration along imaging specific variations of principal component ... canonical correlation analysis...

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...