There are 1 repository under covariance topic.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Covers the basics of mixed models, mostly using @lme4
Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]
I will update this repository to learn Machine learning with python with statistics content and materials
Functions for the construction of risk-based portfolios
Fast & numerically stable statistical analysis
Lightweight robust covariance estimation in Julia
SIMD-enabled descriptive statistics (mean, variance, covariance, correlation)
Kriging estimators for the GeoStats.jl framework
Gaussian process regression
Variography for the GeoStats.jl framework
Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.
Dimensionality reduction on manifold of SPD matrices, based on pymanopt implementation
Built-in solvers for the GeoStats.jl framework
General purpose correlation and covariance estimation
An online machine-learning library for creating robust statistical models of streaming data
Package to calculate the RIE estimator of a correlation matrix
Online statistics
TOMS Algorithm 675: Fortran subroutines for computing the square root covariance filter and square root information filter in dense or Hessenberg forms
Satellite library(fft, variogram,...)
Error propagation with covariant variables
A Step-by-step tutorial to implement PCA.
Geostatistical functions for the GeoStats.jl framework
Analytical Derivation and Comparison of Alarm Similarity Measures Paper Code (IFAC Symposium, ADCHEM Conference 2021)
Machine learning functions written in goLang:
This a tensorflow implementation of VICReg - a self-supervised learning architecture that prevents collapse in an intuitive manner using a loss function that 1. maintains the variance of each embedding over a batch above a threshold and 2. decorrelating pairs of embeddings over a batch and attracting them to 0. Training was done using TPU on colab
Bits, Pieces, Nuts and Bolts on Data Analysis and Machine Learning.
An R package to explore and quality check data
Geostatistical simulation solvers for the GeoStats.jl framework