How does PCA work?
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Updated
Dec 20, 2018 - R
How does PCA work?
FIFA'19 datasets Analysis
Penalized precision matrix estimation via block-wise coordinate descent (graphical lasso)
Penalized precision matrix estimation
Contains some Test of Statistics
Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
Covariance/Correlation for big data in base R
Covariance and correlation matrix via Rhadoop (rmr2 and HDFS)
R Package That Can Simultaneously Perform Factor Analysis And Cluster Analysis Of Count Data Via Parsimonious Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers. This Model Permits For Parsimonious Covariance Structures And Dimension Reduction, Thus Reducing The Number Of Free Parameters To Be Calculated.
R package for statistics of eigenvalue dispersion indices
My Master's thesis on Bayesian Classification with Regularized Gaussian Models
Shrinking characteristics of precision matrix estimators
Generates R code for your lavaan models automatically, which helps with reproducible open science
An R package for testing high-dimensional covariance matrices
R package for adaptive correlation and covariance matrix shrinkage.
Penalized precision matrix estimation via ADMM
This R package is a wrapper around the popular "glasso" package with built-in cross validation and visualizations
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.
Mean and Covariance Matrix Estimation under Heavy Tails
Computation of Sparse Eigenvectors of a Matrix
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