🔗 Methods for Correlation Analysis

Scikit-learn compatible estimation of general graphical models

Bayesian Gaussian Graphical Models

Linear Gaussian Bayesian Networks - Inference, Parameter Learning and Representation. 🖧

Bayesian structure learning and classification in decomposable graphical models.

PCA, Factor Analysis, CCA, Sparse Covariance Matrix Estimation, Imputation, Multiple Hypothesis Testing

This module is a tool for calculating correlations such as Partial, Tetrachoric, Intraclass correlation coefficients, Bootstrap agreement, Rater reliability, Generalizability Theory, Analytic Hierarchy Process, and allows users to produce Gaussian Graphical Model and Partial plot.

Source code for the paper "Fast and Accurate Inference of Gene Regulatory Networks through Robust Precision Matrix Estimation", by Passemiers et al.

Infers species direct association networks

High-dimensional change point detection in Gaussian Graphical models with missing values

Machine Learning 2017 / "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", / https://cran.r-project.org/web/packages/simule/

Monte Carlo Penalty Selection for graphical lasso

AISTAT 2017 Paper: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

GGM structure learning using 1 bit.

A Lightning-fast algorithm for Gene Regulatory Network inference from gene expression data

Computational Studies of Adja Magatte Fall Internship

A Collection of Utilities for Modeling Multivariate Data Using Probabilistic Graphical Models

This aim of this project is to analyze globular star clusters in the Milky Way, in order to understand their dynamics. The conducted study examined the properties that affect the central velocity dispersion, their impact and the correlations between them.