Showing 13 of total 13 results (show query)
convexfi
fitHeavyTail:Mean and Covariance Matrix Estimation under Heavy Tails
Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).
Maintained by Daniel P. Palomar. Last updated 2 years ago.
cauchycovariance-estimationcovariance-matrixheavy-tailed-distributionsoutliersrobust-estimationstudent-ttyler
22 stars 6.27 score 28 scripts 1 dependents
klauschn
ICtest:Estimating and Testing the Number of Interesting Components in Linear Dimension Reduction
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Maintained by Klaus Nordhausen. Last updated 4 days ago.
6.05 score 63 scripts 4 dependents
dppalomar
highOrderPortfolios:Design of High-Order Portfolios Including Skewness and Kurtosis
The classical Markowitz's mean-variance portfolio formulation ignores heavy tails and skewness. High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." <arXiv:2008.00863>. X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." <arXiv:2206.02412>.
Maintained by Daniel P. Palomar. Last updated 2 years ago.
24 stars 5.90 score 22 scripts
bioc
nethet:A bioconductor package for high-dimensional exploration of biological network heterogeneity
Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).
Maintained by Nicolas Staedler. Last updated 5 months ago.
4.30 score 7 scripts
laowang-123
singR:Simultaneous Non-Gaussian Component Analysis
Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.
Maintained by Liangkang Wang. Last updated 2 months ago.
2.59 score 39 scripts
cran
MNM:Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
Maintained by Klaus Nordhausen. Last updated 1 years ago.
2.48 score 1 dependentscran
tsBSS:Blind Source Separation and Supervised Dimension Reduction for Time Series
Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.
Maintained by Markus Matilainen. Last updated 4 years ago.
4 stars 2.38 score 2 dependents
jmvirta
tensorBSS:Blind Source Separation Methods for Tensor-Valued Observations
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Maintained by Joni Virta. Last updated 7 months ago.
1 stars 1.41 score 26 scripts
kolassa-dev
MultNonParam:Multivariate Nonparametric Methods
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.
Maintained by John E. Kolassa. Last updated 2 years ago.
1.18 score 15 scripts
denufl
mvctm:Multivariate Variance Components Tests for Multilevel Data
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
Maintained by Denis Larocque. Last updated 7 years ago.
1.00 score 3 scriptscran
ssaBSS:Stationary Subspace Analysis
Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).
Maintained by Markus Matilainen. Last updated 2 years ago.
1.00 scorecran
ellipticalsymmetry:Elliptical Symmetry Tests
Given the omnipresence of the assumption of elliptical symmetry, it is essential to be able to test whether that assumption actually holds true or not for the data at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2021) <arXiv:2011.12560v2>.
Maintained by Marko Palangetic. Last updated 4 years ago.
1.00 score