Showing 6 of total 6 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
blasif
cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.
Maintained by Federico Blasi. Last updated 2 months ago.
covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationpredictioncpp
3 stars 5.48 score 1 scripts
dppalomar
sparseEigen:Computation of Sparse Eigenvectors of a Matrix
Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing, IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. <doi:10.1109/TSP.2016.2605073>.
Maintained by Daniel P. Palomar. Last updated 6 years ago.
covariance-matrixeigenvectorspcasparse
12 stars 5.42 score 22 scripts
mgallow
CVglasso:Lasso Penalized Precision Matrix Estimation
Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package and extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.
Maintained by Matt Galloway. Last updated 7 years ago.
covariance-matrixglassolassoprecision-matrix
4.97 score 31 scripts 1 dependentsmgallow
ADMMsigma:Penalized Precision Matrix Estimation via ADMM
Estimates a penalized precision matrix via the alternating direction method of multipliers (ADMM) algorithm. It currently supports a general elastic-net penalty that allows for both ridge and lasso-type penalties as special cases. This package is an alternative to the 'glasso' package. See Boyd et al (2010) <doi:10.1561/2200000016> for details regarding the estimation method.
Maintained by Matt Galloway. Last updated 7 years ago.
admmcovariance-matrixglassolassoprecision-matrixridgeopenblascpp
4 stars 4.86 score 12 scripts
anestistouloumis
ShrinkCovMat:Shrinkage Covariance Matrix Estimators
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Maintained by Anestis Touloumis. Last updated 2 years ago.
covariance-matrixshrinkage-estimatorsopenblascppopenmp
8 stars 4.83 score 17 scripts