My Master's thesis on Bayesian Classification with Regularized Gaussian Models
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Updated
Dec 27, 2015 - R
My Master's thesis on Bayesian Classification with Regularized Gaussian Models
Covariance and correlation matrix via Rhadoop (rmr2 and HDFS)
An R package for testing high-dimensional covariance matrices
Covariance/Correlation for big data in base R
Penalized precision matrix estimation
This R package is a wrapper around the popular "glasso" package with built-in cross validation and visualizations
Penalized precision matrix estimation via block-wise coordinate descent (graphical lasso)
Penalized precision matrix estimation via ADMM
Shrinking characteristics of precision matrix estimators
How does PCA work?
Computation of Sparse Eigenvectors of a Matrix
R package for adaptive correlation and covariance matrix shrinkage.
FIFA'19 datasets Analysis
Covariance Matrix Estimation via Factor Models
Generates R code for your lavaan models automatically, which helps with reproducible open science
Contains some Test of Statistics
Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
Mean and Covariance Matrix Estimation under Heavy Tails
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.
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.
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