R-universe search: topic:covariance-estimation

Showing 6 of total 6 results (show query)

bsaul

geex:An API for M-Estimation

Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

Maintained by Bradley Saul. Last updated 11 months ago.

asymptotics covariance-estimates covariance-estimation estimate-parameters estimating-equations estimation inference m-estimation robust sandwich

9 stars 7.75 score 131 scripts 2 dependents

przechoj

gips:Gaussian Model Invariant by Permutation Symmetry

Find the permutation symmetry group such that the covariance matrix of the given data is approximately invariant under it. Discovering such a permutation decreases the number of observations needed to fit a Gaussian model, which is of great use when it is smaller than the number of variables. Even if that is not the case, the covariance matrix found with 'gips' approximates the actual covariance with less statistical error. The methods implemented in this package are described in Graczyk et al. (2022) <doi:10.1214/22-AOS2174>. Documentation about 'gips' is provided via its website at <https://przechoj.github.io/gips/> and the paper by Chojecki, Morgen, Kołodziejek (2025, <doi:10.18637/jss.v112.i07>).

Maintained by Adam Przemysław Chojecki. Last updated 16 days ago.

covariance-estimation machine-learning normal-distribution

6 stars 6.52 score 31 scripts

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.

cauchy covariance-estimation covariance-matrix heavy-tailed-distributions outliers robust-estimation student-t tyler

22 stars 6.27 score 28 scripts 1 dependents

egpivo

SpatPCA:Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm covariance-estimation eigenfunctions lasso matrix-factorization pca rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines openblas cpp openmp

20 stars 5.53 score 17 scripts

kisungyou

CovTools:Statistical Tools for Covariance Analysis

Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

Maintained by Kisung You. Last updated 2 years ago.

covariance covariance-estimation openblas cpp

14 stars 4.59 score 55 scripts

javzapata

fgm:Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes

Estimates a functional graphical model and a partially separable KL decomposition for a multivariate Gaussian process.

Maintained by Javier Zapata. Last updated 4 years ago.

covariance-estimation functional-data-analysis gaussian-processes graphical-models karhunen-loeve neuroimaging-data neuroscience

4 stars 3.30 score 8 scripts