an R package for structural equation modeling and more

Interfaces for Multivariate Analysis in R

Development repository for the Bioconductor package 'mixOmics '

An R package for Bayesian structural equation modeling

Explorative multivariate statistics in Python

A collection of Poisson lognormal models for multivariate count data analysis

lit-element components for fast and modular multivariate analysis

Implementation of a Partial Least Squares Regressor

Flexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.

Estimate dynamic high-order correlations in multivariate timeseries data

metaSEM package

R package for fitting joint models to time-to-event data and multivariate longitudinal data

Markov random fields with covariates

Software for the analysis and interactive exploration of spectral imaging data

MoMA: Modern Multivariate Analysis in R

(Multiblock) Partial Least Squares Regression for Python

metaUSAT is a data-adaptive statistical approach for testing genetic associations of multiple traits from single/multiple studies using univariate GWAS summary statistics.

Julia and Python programs that implement some of the tools described in my book "Stochastic Methods in Asset Pricing" (SMAP), MIT Press 2017 (e.g., the method for computing the price of American call options and the construction of the early exercise premium in the Black-Scholes-Merton framework from section 18.4 in SMAP).

Python implementation of STATIS for analysis of several data tables

Bayesian bi-clustering of categorical data

Geological Reservoir Virtualization

Parkinson's disease data analysis from uci machine learning repository dataset.

📍 Multivariate Data Analysis

Case Study in ranking U.S. cities based on a single linear combination of rating variables. Dimensionality techniques used in the analysis are Principal Component Analysis (PCA), Factor Analysis (FA), Canonical Correlation Analysis (CCA)

🌐 REST Framework

R/Bioconductor package - STRUCT: STatistics in R Using Class Templates

Multivariate Analysis in Python