cvCovEst - Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.
Last updated
covariance-matrix-estimationcross-validationhigh-dimensional-statisticsnonparametric-statistics
6.66 score 14 stars 1 dependents 36 scripts 334 downloadsscPCA - Sparse Contrastive Principal Component Analysis
A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.
Last updated
principalcomponentgeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqbioconductorcontrastive-learningdimensionality-reduction
6.33 score 12 stars 36 scripts 406 downloadsneatmaps - Heatmaps for Multiple Network Data
Simplify the exploratory data analysis process for multiple network data sets with the help of hierarchical clustering, consensus clustering and heatmaps. Multiple network data consists of multiple disjoint networks that have common variables (e.g. ego networks). This package contains the necessary tools for exploring such data, from the data pre-processing stage to the creation of dynamic visualizations.
Last updated
2.70 score 1 stars 7 scripts 206 downloads